Patent Publication Number: US-9898224-B1

Title: Automatic adjustment of capacity usage by data storage optimizer for data migration

Description:
BACKGROUND 
     Technical Field 
     This application generally relates to data storage, and more particularly to techniques used in connection with performing data migration. 
     Description of Related Art 
     Computer systems may include different resources used by one or more host processors. Resources and host processors in a computer system may be interconnected by one or more communication connections. These resources may include, for example, data storage devices such as those included in the data storage systems manufactured by EMC Corporation. These data storage systems may be coupled to one or more host processors and provide storage services to each host processor. Multiple data storage systems from one or more different vendors may be connected and may provide common data storage for one or more host processors in a computer system. 
     A host processor may perform a variety of data processing tasks and operations using the data storage system. For example, a host processor may perform basic system I/O operations in connection with data requests, such as data read and write operations. 
     Host processor systems may store and retrieve data using a storage device containing a plurality of host interface units, disk drives, and disk interface units. Such storage devices are provided, for example, by EMC Corporation of Hopkinton, Mass. The host systems access the storage device through a plurality of channels provided therewith. Host systems provide data and access control information through the channels to the storage device and storage device provides data to the host systems also through the channels. The host systems do not address the disk drives of the storage device directly, but rather, access what appears to the host systems as a plurality of logical disk units, logical devices, or logical volumes (LVs). The logical disk units may or may not correspond to the actual disk drives. Allowing multiple host systems to access the single storage device unit allows the host systems to share data stored therein. 
     In connection with data storage, a variety of different technologies may be used. Data may be stored, for example, on different types of disk devices and/or flash memory devices. The data storage environment may define multiple storage tiers in which each tier includes physical devices or drives of varying technologies, performance characteristics, and the like. The physical devices of a data storage system, such as a data storage array, may be used to store data for multiple applications. 
     SUMMARY OF THE INVENTION 
     In accordance with one aspect of the invention is a method of migrating data comprising: receiving a first message at a target data storage system from a source data storage system, the target data storage system including a data storage optimizer that performs automated data movement optimizations moving data portions between different ones of a plurality of storage tiers in accordance with activity levels associated with the data portions, each of the plurality of storage tiers having a different performance classification, wherein the first message requests a reservation of a first amount of storage on a first of the plurality of storage tiers data storage system for performing a data migration to migrate data from the source data storage system to the target data storage system; performing first processing by the data storage optimizer of the target data storage system in response to receiving the first message, the first processing including: reducing a first capacity limit of the first storage tier by said first amount thereby representing the reservation of the first amount of storage for performing the data migration, said first capacity limit representing a maximum capacity of the first storage tier used by the data storage optimizer; determining whether the first storage tier includes a current amount of available storage that is at least the first amount; and if the current amount of available storage of the first storage tier is not at least the first amount, performing second processing to increase the current amount of available storage of the first storage to the first amount by moving data portions currently stored in the first storage tier to one or more others of said plurality of storage tiers; and in response to increasing the current amount of available storage of the first storage tier to the first amount, sending a second message from the target data storage system to the source data storage system to start the data migration. The method may include after completion of the data migration, sending a third message from the source data storage system to the target data storage system to indicate said completion. The second processing may include selecting a first set of data portions currently stored in the first storage tier, the first set of data portion including a number of data portions ranked as having a lower activity level than any other data portion stored in the first tier that is not included in the first set; and moving data portions of the first set from the first storage tier having a first performance classification to a second storage tier having a second performance classification that is lower than the first performance classification. The second processing may also include selecting a first set of data portions currently stored in the first storage tier, the first set of data portion including a number of data portions ranked as having a higher activity level than any other data portion stored in the first tier that is not included in the first set; and moving data portions of the first set from the first storage tier having a first performance classification to a second storage tier having a second performance classification that is higher than the first performance classification. The plurality of storage tiers may be partitioned into a plurality of performance categories, each of said plurality of performance categories including one or more of said plurality of storage tiers, wherein the first storage tier and a second of the plurality of storage tiers are included in a first of the performance categories. The second processing may include selecting a first set of data portions currently stored in the first storage tier; and moving data portions of the first set from the first storage tier to the second storage tier included in the first performance category. After receiving the third message indicating that the data migration has completed, the target data storage system may increase the first capacity limit of the first storage tier by the first amount. Each of the one or more others of said plurality of storage tiers may include a capacity limit denoting a maximum capacity of said each storage tier usable by the data storage optimizer and wherein data movements performed in connection with said second processing to move data portions from the first storage tier to said each storage tier may be performed subject to the capacity limit of said each storage tier. Each of the one or more others of said plurality of storage tiers may include a performance limit for said each storage tier and wherein data movements performed in connection with said second processing to move data portions from the first storage tier to said each storage tier may be performed subject to the performance limit of said each storage tier. The plurality of storage tiers may include at least three storage tiers and wherein at least one of the plurality of storage tiers may include solid state storage devices and at least one of the plurality of storage tiers included rotating disk devices. The second processing may select data portions of the first storage tier for data movement based on activity levels of the data portions. An activity level of each data portion in the first storage tier may be a score determined as a weighting of one or more performance metrics for said each data portion, and wherein the one or more performance metrics may include short term performance metrics and long term performance metrics. The target data storage system may be connected to an external data storage system having physical storage devices managed by the data storage optimizer of the target data storage system. The first storage tier may be specified as a target tier for storing migrated data from the source data storage system and may be included in the external data storage system. 
     In accordance with another aspects of the invention is a computer readable medium comprising code stored thereon for migrating data, the computer readable medium comprising code for: receiving a first message at a target data storage system from a source data storage system, the target data storage system including a data storage optimizer that performs automated data movement optimizations moving data portions between different ones of a plurality of storage tiers in accordance with activity levels associated with the data portions, each of the plurality of storage tiers having a different performance classification, wherein the first message requests a reservation of a first amount of storage on a first of the plurality of storage tiers data storage system for performing a data migration to migrate data from the source data storage system to the target data storage system; performing first processing by the data storage optimizer of the target data storage system in response to receiving the first message, the first processing including: reducing a first capacity limit of the first storage tier by said first amount thereby representing the reservation of the first amount of storage for performing the data migration, said first capacity limit representing a maximum capacity of the first storage tier used by the data storage optimizer; determining whether the first storage tier includes a current amount of available storage that is at least the first amount; and if the current amount of available storage of the first storage tier is not at least the first amount, performing second processing to increase the current amount of available storage of the first storage to the first amount by moving data portions currently stored in the first storage tier to one or more others of said plurality of storage tiers; in response to increasing the current amount of available storage of the first storage tier to the first amount, sending a second message from the target data storage system to the source data storage system to start the data migration; and after completion of the data migration, sending a third message from the source data storage system to the target data storage system to indicate said completion. The computer readable medium may also include code that, after completion of the data migration, sends a third message from the source data storage system to the target data storage system to indicate said completion. The second processing may also include selecting a first set of data portions currently stored in the first storage tier, the first set of data portion including a number of data portions ranked as having a lower activity level than any other data portion stored in the first tier that is not included in the first set; and moving data portions of the first set from the first storage tier having a first performance classification to a second storage tier having a second performance classification that is lower than the first performance classification. The second processing may also include selecting a first set of data portions currently stored in the first storage tier, the first set of data portion including a number of data portions ranked as having a higher activity level than any other data portion stored in the first tier that is not included in the first set; and moving data portions of the first set from the first storage tier having a first performance classification to a second storage tier having a second performance classification that is higher than the first performance classification. After receiving the third message indicating that the data migration has completed, the target data storage system may increase the first capacity limit of the first storage tier by the first amount. Each of the one or more others of said plurality of storage tiers may include a capacity limit denoting a maximum capacity of said each storage tier usable by the data storage optimizer and wherein data movements performed in connection with said second processing to move data portions from the first storage tier to said each storage tier may be performed subject to the capacity limit of said each storage tier. 
     In accordance with another aspect of the invention is a system comprising: a source data storage system including a migration process stored in first memory of the source data storage system, said migration process controlling migration of data from the source data storage system to the target data storage system; the target data storage system including a data storage optimizer stored in second memory of the target data storage system, the data storage optimizer performing automated data movement optimizations moving data portions between different ones of a plurality of storage tiers in accordance with activity levels associated with the data portions, each of the plurality of storage tiers having a different performance classification, and wherein said second memory further includes code, which when executed, performs processing including: receiving a first message at the target data storage system from the source data storage system, said first message requesting a reservation of a first amount of storage on a first of the plurality of storage tiers data storage system for performing a data migration to migrate data from the source data storage system to the target data storage system; reducing a first capacity limit of the first storage tier by said first amount thereby representing the reservation of the first amount of storage for performing the data migration, said first capacity limit representing a maximum capacity of the first storage tier used by the data storage optimizer; determining whether the first storage tier includes a current amount of available storage that is at least the first amount; and if the current amount of available storage of the first storage tier is not at least the first amount, increasing the current amount of available storage of the first storage to the first amount by moving data portions currently stored in the first storage tier to one or more others of said plurality of storage tiers; in response to increasing the current amount of available storage of the first storage tier to the first amount, sending a second message from the target data storage system to the source data storage system to start the data migration; and receiving a third message from the source data storage system at the target data storage system indicating completion of the data migration. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Features and advantages of the present invention will become more apparent from the following detailed description of exemplary embodiments thereof taken in conjunction with the accompanying drawings in which: 
         FIG. 1  is an example of an embodiment of a system that may utilize the techniques described herein; 
         FIG. 2  is a representation of the logical internal communications between the directors and memory included in one embodiment of a data storage system of  FIG. 1 ; 
         FIG. 3  is an example representing components that may be included in a service processor in an embodiment in accordance with techniques herein; 
         FIGS. 4, 5A and 5B  are examples illustrating a data storage system, such as data storage array, including a plurality of storage tiers in an embodiment in accordance with techniques herein; 
         FIG. 5C  is a schematic diagram illustrating tables that are used to keep track of device information in connection with an embodiment of the system described herein; 
         FIG. 5D  is a schematic diagram showing a group element of a thin device table in connection with an embodiment of the system described herein; 
         FIGS. 6 and 7  are examples illustrating a storage group, allocation policy and associated storage tiers in an embodiment in accordance with techniques herein; 
         FIGS. 8A and 8B  are examples illustrating thin devices and associated structures that may be used in an embodiment in accordance with techniques herein; 
         FIG. 9  is an example illustrating data portions comprising a thin device&#39;s logical address range; 
         FIG. 10  is an example of performance information that may be determined in connection with thin devices in an embodiment in accordance with techniques herein; 
         FIG. 11  is a graphical illustration of long term and short term statistics described herein; 
         FIGS. 12, 15, 17, 18, 19, 24 and 25  are flowcharts of processing steps that may be performed in an embodiment in accordance with techniques herein; 
         FIGS. 13 and 13A-13E  are examples of performance curves that may be used to model device response time and in selection of weights for scoring calculations in an embodiment in accordance with techniques herein; 
         FIGS. 14, 14A and 16  illustrate histograms that may be used in threshold selection in accordance with techniques herein; 
         FIG. 20  is an example illustrating messages that may be exchanged between an external data storage system and a target data storage system in connection with performing a migration to the target system in an embodiment in accordance with techniques herein; 
         FIG. 21  is an example illustrating capacity limits and amounts of storage used by the optimizer in an embodiment in accordance with techniques herein; 
         FIG. 22  is a histogram illustrating scores of the data portions stored in different ones of the storage tiers in the target system in an embodiment in accordance with techniques herein; 
         FIG. 23  is an example of partitioning storage tiers into a plurality of performance categories or classifications whereby each such category includes one or more storage tiers of the target system in an embodiment in accordance with techniques herein; and 
         FIGS. 26 and 27  are exemplary embodiments in which the data storage optimizer manages storage across multiple data storage systems including external data storage systems in accordance with techniques herein. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENT(S) 
     Referring to  FIG. 1 , shown is an example of an embodiment of a system that may be used in connection with performing the techniques described herein. The system  10  includes a data storage system  12  connected to host systems  14   a - 14   n  through communication medium  18 . In this embodiment of the computer system  10 , and the n hosts  14   a - 14   n  may access the data storage system  12 , for example, in performing input/output (I/O) operations or data requests. The communication medium  18  may be any one or more of a variety of networks or other type of communication connections as known to those skilled in the art. The communication medium  18  may be a network connection, bus, and/or other type of data link, such as a hardwire or other connections known in the art. For example, the communication medium  18  may be the Internet, an intranet, network (including a Storage Area Network (SAN)) or other wireless or other hardwired connection(s) by which the host systems  14   a - 14   n  may access and communicate with the data storage system  12 , and may also communicate with other components included in the system  10 . 
     Each of the host systems  14   a - 14   n  and the data storage system  12  included in the system  10  may be connected to the communication medium  18  by any one of a variety of connections as may be provided and supported in accordance with the type of communication medium  18 . The processors included in the host computer systems  14   a - 14   n  may be any one of a variety of proprietary or commercially available single or multi-processor system, such as an Intel-based processor, or other type of commercially available processor able to support traffic in accordance with each particular embodiment and application. 
     It should be noted that the particular examples of the hardware and software that may be included in the data storage system  12  are described herein in more detail, and may vary with each particular embodiment. Each of the host computers  14   a - 14   n  and data storage system may all be located at the same physical site, or, alternatively, may also be located in different physical locations. Examples of the communication medium that may be used to provide the different types of connections between the host computer systems and the data storage system of the system  10  may use a variety of different communication protocols such as SCSI, Fibre Channel, iSCSI, and the like. Some or all of the connections by which the hosts and data storage system may be connected to the communication medium may pass through other communication devices, such switching equipment that may exist such as a phone line, a repeater, a multiplexer or even a satellite. 
     Each of the host computer systems may perform different types of data operations in accordance with different types of tasks. In the embodiment of  FIG. 1 , any one of the host computers  14   a - 14   n  may issue a data request to the data storage system  12  to perform a data operation. For example, an application executing on one of the host computers  14   a - 14   n  may perform a read or write operation resulting in one or more data requests to the data storage system  12 . 
     It should be noted that although element  12  is illustrated as a single data storage system, such as a single data storage array, element  12  may also represent, for example, multiple data storage arrays alone, or in combination with, other data storage devices, systems, appliances, and/or components having suitable connectivity, such as in a SAN, in an embodiment using the techniques herein. It should also be noted that an embodiment may include data storage arrays or other components from one or more vendors. In subsequent examples illustrated the techniques herein, reference may be made to a single data storage array by a vendor, such as by EMC Corporation of Hopkinton, Mass. However, as will be appreciated by those skilled in the art, the techniques herein are applicable for use with other data storage arrays by other vendors and with other components than as described herein for purposes of example. 
     The data storage system  12  may be a data storage array including a plurality of data storage devices  16   a - 16   n . The data storage devices  16   a - 16   n  may include one or more types of data storage devices such as, for example, one or more disk drives and/or one or more solid state drives (SSDs). An SSD is a data storage device that uses solid-state memory to store persistent data. An SSD using SRAM or DRAM, rather than flash memory, may also be referred to as a RAM drive. SSD may refer to solid state electronics devices as distinguished from electromechanical devices, such as hard drives, having moving parts. Flash devices or flash memory-based SSDs are one type of SSD that contains no moving parts. As described in more detail in following paragraphs, the techniques herein may be used in an embodiment in which one or more of the devices  16   a - 16   n  are flash drives or devices. More generally, the techniques herein may also be used with any type of SSD although following paragraphs may make reference to a particular type such as a flash device or flash memory device. 
     The data storage array may also include different types of adapters or directors, such as an HA  21  (host adapter), RA  40  (remote adapter), and/or device interface  23 . Each of the adapters may be implemented using hardware including a processor with local memory with code stored thereon for execution in connection with performing different operations. The HAs may be used to manage communications and data operations between one or more host systems and the global memory (GM). In an embodiment, the HA may be a Fibre Channel Adapter (FA) or other adapter which facilitates host communication. The HA  21  may be characterized as a front end component of the data storage system which receives a request from the host. The data storage array may include one or more RAs that may be used, for example, to facilitate communications between data storage arrays. The data storage array may also include one or more device interfaces  23  for facilitating data transfers to/from the data storage devices  16   a - 16   n . The data storage interfaces  23  may include device interface modules, for example, one or more disk adapters (DAs) (e.g., disk controllers), adapters used to interface with the flash drives, and the like. The DAs may also be characterized as back end components of the data storage system which interface with the physical data storage devices. 
     One or more internal logical communication paths may exist between the device interfaces  23 , the RAs  40 , the HAs  21 , and the memory  26 . An embodiment, for example, may use one or more internal busses and/or communication modules. For example, the global memory portion  25   b  may be used to facilitate data transfers and other communications between the device interfaces, HAs and/or RAs in a data storage array. In one embodiment, the device interfaces  23  may perform data operations using a cache that may be included in the global memory  25   b , for example, when communicating with other device interfaces and other components of the data storage array. The other portion  25   a  is that portion of memory that may be used in connection with other designations that may vary in accordance with each embodiment. 
     The particular data storage system as described in this embodiment, or a particular device thereof, such as a disk or particular aspects of a flash device, should not be construed as a limitation. Other types of commercially available data storage systems, as well as processors and hardware controlling access to these particular devices, may also be included in an embodiment. 
     Host systems provide data and access control information through channels to the storage systems, and the storage systems may also provide data to the host systems also through the channels. The host systems do not address the drives or devices  16   a - 16   n  of the storage systems directly, but rather access to data may be provided to one or more host systems from what the host systems view as a plurality of logical devices or logical volumes (LVs). The LVs may or may not correspond to the actual physical devices or drives  16   a - 16   n . For example, one or more LVs may reside on a single physical drive or multiple drives. Data in a single data storage system, such as a single data storage array, may be accessed by multiple hosts allowing the hosts to share the data residing therein. The HAs may be used in connection with communications between a data storage array and a host system. The RAs may be used in facilitating communications between two data storage arrays. The DAs may be one type of device interface used in connection with facilitating data transfers to/from the associated disk drive(s) and LV(s) residing thereon. A flash device interface may be another type of device interface used in connection with facilitating data transfers to/from the associated flash devices and LV(s) residing thereon. It should be noted that an embodiment may use the same or a different device interface for one or more different types of devices than as described herein. 
     The device interface, such as a DA, performs I/O operations on a drive  16   a - 16   n . In the following description, data residing on an LV may be accessed by the device interface following a data request in connection with I/O operations that other directors originate. Data may be accessed by LV in which a single device interface manages data requests in connection with the different one or more LVs that may reside on a drive  16   a - 16   n . For example, a device interface may be a DA that accomplishes the foregoing by creating job records for the different LVs associated with a particular device. These different job records may be associated with the different LVs in a data structure stored and managed by each device interface. 
     Also shown in  FIG. 1  is a service processor  22   a  that may be used to manage and monitor the system  12 . In one embodiment, the service processor  22   a  may be used in collecting performance data, for example, regarding the I/O performance in connection with data storage system  12 . This performance data may relate to, for example, performance measurements in connection with a data request as may be made from the different host computer systems  14   a    14   n . This performance data may be gathered and stored in a storage area. Additional detail regarding the service processor  22   a  is described in following paragraphs. 
     It should be noted that a service processor  22   a  may exist external to the data storage system  12  and may communicate with the data storage system  12  using any one of a variety of communication connections. In one embodiment, the service processor  22   a  may communicate with the data storage system  12  through three different connections, a serial port, a parallel port and using a network interface card, for example, with an Ethernet connection. Using the Ethernet connection, for example, a service processor may communicate directly with DAs and HAs within the data storage system  12 . 
     Referring to  FIG. 2 , shown is a representation of the logical internal communications between the directors and memory included in a data storage system. Included in  FIG. 2  is a plurality of directors  37   a - 37   n  coupled to the memory  26 . Each of the directors  37   a - 37   n  represents one of the HAs, RAs, or device interfaces that may be included in a data storage system. In an embodiment disclosed herein, there may be up to sixteen directors coupled to the memory  26 . Other embodiments may allow a maximum number of directors other than sixteen as just described and the maximum number may vary with embodiment. 
     The representation of  FIG. 2  also includes an optional communication module (CM)  38  that provides an alternative communication path between the directors  37   a - 37   n . Each of the directors  37   a - 37   n  may be coupled to the CM  38  so that any one of the directors  37   a - 37   n  may send a message and/or data to any other one of the directors  37   a - 37   n  without needing to go through the memory  26 . The CM  38  may be implemented using conventional MUX/router technology where a sending one of the directors  37   a - 37   n  provides an appropriate address to cause a message and/or data to be received by an intended receiving one of the directors  37   a - 37   n . In addition, a sending one of the directors  37   a - 37   n  may be able to broadcast a message to all of the other directors  37   a - 37   n  at the same time. 
     With reference back to  FIG. 1 , components of the data storage system may communicate using GM  25   b . For example, in connection with a write operation, an embodiment may first store the data in cache included in a portion of GM  25   b , mark the cache slot including the write operation data as write pending (WP), and then later destage the WP data from cache to one of the devices  16   a - 16   n . In connection with returning data to a host from one of the devices as part of a read operation, the data may be copied from the device by the appropriate device interface, such as a DA servicing the device. The device interface may copy the data read into a cache slot included in GM which is, in turn, communicated to the appropriate HA in communication with the host. 
     As described above, the data storage system  12  may be a data storage array including a plurality of data storage devices  16   a - 16   n  in which one or more of the devices  16   a - 16   n  are flash memory devices employing one or more different flash memory technologies. In one embodiment, the data storage system  12  may be a Symmetrix® DMX™ or VMAX™ data storage array by EMC Corporation of Hopkinton, Mass. In the foregoing data storage array, the data storage devices  16   a - 16   n  may include a combination of disk devices and flash devices in which the flash devices may appear as standard Fibre Channel (FC) drives to the various software tools used in connection with the data storage array. The flash devices may be constructed using nonvolatile semiconductor NAND flash memory. The flash devices may include one or more SLC (single level cell) devices and/or MLC (multi level cell) devices. 
     It should be noted that the techniques herein may be used in connection with flash devices comprising what may be characterized as enterprise-grade or enterprise-class flash drives (EFDs) with an expected lifetime (e.g., as measured in an amount of actual elapsed time such as a number of years, months, and/or days) based on a number of guaranteed write cycles, or program cycles, and a rate or frequency at which the writes are performed. Thus, a flash device may be expected to have a usage measured in calendar or wall clock elapsed time based on the amount of time it takes to perform the number of guaranteed write cycles. The techniques herein may also be used with other flash devices, more generally referred to as non-enterprise class flash devices, which, when performing writes at a same rate as for enterprise class drives, may have a lower expected lifetime based on a lower number of guaranteed write cycles. 
     The techniques herein may be generally used in connection with any type of flash device, or more generally, any SSD technology. The flash device may be, for example, a flash device which is a NAND gate flash device, NOR gate flash device, flash device that uses SLC or MLC technology, and the like, as known in the art. In one embodiment, the one or more flash devices may include MLC flash memory devices although an embodiment may utilize MLC, alone or in combination with, other types of flash memory devices or other suitable memory and data storage technologies. More generally, the techniques herein may be used in connection with other SSD technologies although particular flash memory technologies may be described herein for purposes of illustration. 
     An embodiment in accordance with techniques herein may have one or more defined storage tiers. Each tier may generally include physical storage devices or drives having one or more attributes associated with a definition for that tier. For example, one embodiment may provide a tier definition based on a set of one or more attributes. The attributes may include any one or more of a storage type or storage technology, a type of data protection, device performance characteristic(s), storage capacity, and the like. The storage type or technology may specify whether a physical storage device is an SSD drive (such as a flash drive), a particular type of SSD drive (such using flash or a form of RAM), a type of magnetic disk or other non-SSD drive (such as an FC disk drive, a SATA (Serial Advanced Technology Attachment) drive), and the like. Data protection may specify a type or level of data storage protection such, for example, as a particular RAID level (e.g., RAID1, RAID-5 3+1, RAIDS 7+1, and the like). Performance characteristics may relate to different performance aspects of the physical storage devices of a particular type or technology. For example, there may be multiple types of FC disk drives based on the RPM characteristics of the FC disk drives (e.g., 10K RPM FC drives and 15K RPM FC drives) and FC disk drives having different RPM characteristics may be included in different storage tiers. Storage capacity may specify the amount of data, such as in bytes, that may be stored on the drives. An embodiment may allow a user to define one or more such storage tiers. For example, an embodiment in accordance with techniques herein may define two storage tiers including a first tier of all SSD drives and a second tier of all non-SSD drives. As another example, an embodiment in accordance with techniques herein may define three storage tiers including a first tier of all SSD drives which are flash drives, a second tier of all FC drives, and a third tier of all SATA drives. The foregoing are some examples of tier definitions and other tier definitions may be specified in accordance with techniques herein. 
     Referring to  FIG. 3 , shown is an example  100  of software that may be included in a service processor such as  22   a . It should be noted that the service processor may be any one of a variety of commercially available processors, such as an Intel-based processor, and the like. Although what is described herein shows details of software that may reside in the service processor  22   a , all or portions of the illustrated components may also reside elsewhere such as, for example, on any of the host systems  14   a    14   n.    
     Included in the service processor  22   a  is performance data monitoring software  134  which gathers performance data about the data storage system  12  through the connection  132 . The performance data monitoring software  134  gathers and stores performance data and forwards this to the optimizer  138  which further stores the data in the performance data file  136 . This performance data  136  may also serve as an input to the optimizer  138  which attempts to enhance the performance of I/O operations, such as those I/O operations associated with data storage devices  16   a - 16   n  of the system  12 . The optimizer  138  may take into consideration various types of parameters and performance data  136  in an attempt to optimize particular metrics associated with performance of the data storage system  12 . The performance data  136  may be used by the optimizer to determine metrics described and used in connection with techniques herein. The optimizer may access the performance data, for example, collected for a plurality of LVs when performing a data storage optimization. The performance data  136  may be used in determining a workload for one or more physical devices, logical devices or volumes (LVs) serving as data devices, thin devices (described in more detail elsewhere herein) or other virtually provisioned devices, portions of thin devices, and the like. The workload may also be a measurement or level of “how busy” a device is, for example, in terms of I/O operations (e.g., I/O throughput such as number of I/Os/second, response time (RT), and the like). 
     The response time for a storage device or volume may be based on a response time associated with the storage device or volume for a period of time. The response time may based on read and write operations directed to the storage device or volume. Response time represents the amount of time it takes the storage system to complete an I/O request (e.g., a read or write request). Response time may be characterized as including two components: service time and wait time. Service time is the actual amount of time spent servicing or completing an I/O request after receiving the request from a host via an HA  21 , or after the storage system  12  generates the I/O request internally. The wait time is the amount of time the I/O request spends waiting in line or queue waiting for service (e.g., prior to executing the I/O operation). 
     It should be noted that the operations of read and write with respect to an LV, thin device, and the like, may be viewed as read and write requests or commands from the DA  23 , controller or other backend physical device interface. Thus, these are operations may also be characterized as a number of operations with respect to the physical storage device (e.g., number of physical device reads, writes, and the like, based on physical device accesses). This is in contrast to observing or counting a number of particular type of I/O requests (e.g., reads or writes) as issued from the host and received by a front end component such as an HA  21 . To illustrate, a host read request may not result in a read request or command issued to the DA if there is a cache hit and the requested data is in cache. The host read request results in a read request or command issued to the DA  23  to retrieve data from the physical drive only if there is a read miss. Furthermore, when writing data of a received host I/O request to the physical device, the host write request may result in multiple reads and/or writes by the DA  23  in addition to writing out the host or user data of the request. For example, if the data storage system implements a RAID data protection technique, such as RAID-5, additional reads and writes may be performed such as in connection with writing out additional parity information for the user data. Thus, observed data gathered to determine workload, such as observed numbers of reads and writes, may refer to the read and write requests or commands performed by the DA. Such read and write commands may correspond, respectively, to physical device accesses such as disk reads and writes that may result from a host I/O request received by an HA  21 . 
     The optimizer  138  may perform processing of the techniques herein set forth in following paragraphs to determine how to allocate or partition physical storage in a multi-tiered environment for use by multiple applications. The optimizer  138  may also perform other processing such as, for example, to determine what particular portions of thin devices to store on physical devices of a particular tier, evaluate when to migrate or move data between physical drives of different tiers, and the like. It should be noted that the optimizer  138  may generally represent one or more components that perform processing as described herein as well as one or more other optimizations and other processing that may be performed in an embodiment. 
     Described in following paragraphs are techniques that may be performed to determine promotion and demotion thresholds (described below in more detail) used in determining what data portions of thin devices to store on physical devices of a particular tier in a multi-tiered storage environment. Such data portions of a thin device may be automatically placed in a storage tier where the techniques herein have determined the storage tier is best to service that data in order to improve data storage system performance. The data portions may also be automatically relocated or migrated to a different storage tier as the work load and observed performance characteristics for the data portions change over time. In accordance with techniques herein, analysis of performance data for data portions of thin devices may be performed in order to determine whether particular data portions should have their data contents stored on physical devices located in a particular storage tier. The techniques herein may take into account how “busy” the data portions are in combination with defined capacity limits and defined performance limits (e.g., such as I/O throughput or I/Os per unit of time, response time, utilization, and the like) associated with a storage tier in order to evaluate which data to store on drives of the storage tier. The foregoing defined capacity limits and performance limits may be used as criteria to determine promotion and demotion thresholds based on projected or modeled I/O workload of a storage tier. Different sets of performance limits, also referred to as comfort performance zones or performance zones, may be evaluated in combination with capacity limits based on one or more overall performance metrics (e.g., average response time across all storage tiers for one or more storage groups) in order to select the promotion and demotion thresholds for the storage tiers. 
     Promotion may refer to movement of data from a first storage tier to a second storage tier where the second storage tier is characterized as having devices of higher performance than devices of the first storage tier. Demotion may refer generally to movement of data from a first storage tier to a second storage tier where the first storage tier is characterized as having devices of higher performance than devices of the second storage tier. As such, movement of data from a first tier of flash devices to a second tier of FC devices and/or SATA devices may be characterized as a demotion and movement of data from the foregoing second tier to the first tier a promotion. The promotion and demotion thresholds refer to thresholds used in connection with data movement. 
     As described in following paragraphs, one embodiment may use an allocation policy specifying an upper limit or maximum threshold of storage capacity for each of one or more tiers for use with an application. The partitioning of physical storage of the different storage tiers among the applications may be initially performed using techniques herein in accordance with the foregoing thresholds of the application&#39;s allocation policy and other criteria. In accordance with techniques herein, an embodiment may determine amounts of the different storage tiers used to store an application&#39;s data, and thus the application&#39;s storage group, subject to the allocation policy and other criteria. Such criteria may also include one or more performance metrics indicating a workload of the application. For example, an embodiment may determine one or more performance metrics using collected or observed performance data for a plurality of different logical devices, and/or portions thereof, used by the application. Thus, the partitioning of the different storage tiers among multiple applications may also take into account the workload or how “busy” an application is. Such criteria may also include capacity limits specifying how much of each particular storage tier may be used to store data for the application&#39;s logical devices. As described in various embodiments herein, the criteria may include one or more performance metrics in combination with capacity limits, performance metrics alone without capacity limits, or capacity limits alone without performance metrics. Of course, as will be appreciated by those of ordinary skill in the art, such criteria may include any of the foregoing in combination with other suitable criteria. 
     As an example, the techniques herein may be described with reference to a storage environment having three storage tiers—a first tier of only flash drives in the data storage system, a second tier of only FC disk drives, and a third tier of only SATA disk drives. In terms of performance, the foregoing three tiers may be ranked from highest to lowest as follows: first, second, and then third. The lower the tier ranking, the lower the tier&#39;s performance characteristics (e.g., longer latency times, capable of less I/O throughput/second/GB (or other storage unit), and the like). Generally, different types of physical devices or physical drives have different types of characteristics. There are different reasons why one may want to use one storage tier and type of drive over another depending on criteria, goals and the current performance characteristics exhibited in connection with performing I/O operations. For example, flash drives of the first tier may be a best choice or candidate for storing data which may be characterized as I/O intensive or “busy” thereby experiencing a high rate of I/Os to frequently access the physical storage device containing the LV&#39;s data. However, flash drives tend to be expensive in terms of storage capacity. SATA drives may be a best choice or candidate for storing data of devices requiring a large storage capacity and which are not I/O intensive with respect to access and retrieval from the physical storage device. The second tier of FC disk drives may be characterized as “in between” flash drives and SATA drives in terms of cost/GB and I/O performance. Thus, in terms of relative performance characteristics, flash drives may be characterized as having higher performance than both FC and SATA disks, and FC disks may be characterized as having a higher performance than SATA. 
     Since flash drives of the first tier are the best suited for high throughput/sec/GB, processing may be performed to determine which of the devices, and portions thereof, are characterized as most I/O intensive and therefore may be good candidates to have their data stored on flash drives. Similarly, the second most I/O intensive devices, and portions thereof, may be good candidates to store on FC disk drives of the second tier and the least I/O intensive devices may be good candidates to store on SATA drives of the third tier. As such, workload for an application may be determined using some measure of I/O intensity, performance or activity (e.g., I/O throughput/second, percentage of read operation, percentage of write operations, response time, etc.) of each device used for the application&#39;s data. Some measure of workload may be used as a factor or criterion in combination with others described herein for determining what data portions are located on the physical storage devices of each of the different storage tiers. 
       FIG. 4  is a schematic illustration showing a storage system  150  that may be used in connection with an embodiment of the system described herein. The storage system  150  may include a storage array  124  having multiple directors  130 - 132  and multiple storage volumes (LVs, logical devices or VOLUMES 0-3)  110 - 113 . Host applications  140 - 144  and/or other entities (e.g., other storage devices, SAN switches, etc.) request data writes and data reads to and from the storage array  124  that are facilitated using one or more of the directors  130 - 132 . The storage array  124  may include similar features as that discussed above. 
     The volumes  110 - 113  may be provided in multiple storage tiers (TIERS 0-3) that may have different storage characteristics, such as speed, cost, reliability, availability, security and/or other characteristics. As described above, a tier may represent a set of storage resources, such as physical storage devices, residing in a storage platform. Examples of storage disks that may be used as storage resources within a storage array of a tier may include sets SATA disks, FC disks and/or EFDs, among other known types of storage devices. 
     According to various embodiments, each of the volumes  110 - 113  may be located in different storage tiers. Tiered storage provides that data may be initially allocated to a particular fast volume/tier, but a portion of the data that has not been used over a period of time (for example, three weeks) may be automatically moved to a slower (and perhaps less expensive) tier. For example, data that is expected to be used frequently, for example database indices, may be initially written directly to fast storage whereas data that is not expected to be accessed frequently, for example backup or archived data, may be initially written to slower storage. In an embodiment, the system described herein may be used in connection with a Fully Automated Storage Tiering (FAST) product produced by EMC Corporation of Hopkinton, Mass., that provides for the optimization of the use of different storage tiers including the ability to easily create and apply tiering policies (e.g., allocation policies, data movement policies including promotion and demotion thresholds, and the like) to transparently automate the control, placement, and movement of data within a storage system based on business needs. The techniques herein may be used to determine amounts or allocations of each storage tier used by each application based on capacity limits in combination with performance limits. 
     Referring to  FIG. 5A , shown is a schematic diagram of the storage array  124  as including a plurality of data devices  61 - 67  communicating with directors  131 - 133 . The data devices  61 - 67  may be implemented as logical devices like standard logical devices (also referred to as thick devices) provided in a Symmetrix® data storage device produced by EMC Corporation of Hopkinton, Mass., for example. In some embodiments, the data devices  61 - 67  may not be directly useable (visible) to hosts coupled to the storage array  124 . Each of the data devices  61 - 67  may correspond to a portion (including a whole portion) of one or more of the disk drives  42 - 44  (or more generally physical devices). Thus, for example, the data device section  61  may correspond to the disk drive  42 , may correspond to a portion of the disk drive  42 , or may correspond to a portion of the disk drive  42  and a portion of the disk drive  43 . The data devices  61 - 67  may be designated as corresponding to different classes, so that different ones of the data devices  61 - 67  correspond to different physical storage having different relative access speeds or RAID protection type (or some other relevant distinguishing characteristic or combination of characteristics), as further discussed elsewhere herein. Alternatively, in other embodiments that may be used in connection with the system described herein, instead of being separate devices, the data devices  61 - 67  may be sections of one data device. 
     As shown in  FIG. 5B , the storage array  124  may also include a plurality of thin devices  71 - 74  that may be adapted for use in connection with the system described herein when using thin provisioning. In a system using thin provisioning, the thin devices  71 - 74  may appear to a host coupled to the storage array  124  as one or more logical volumes (logical devices) containing contiguous blocks of data storage. Each of the thin devices  71 - 74  may contain pointers to some or all of the data devices  61 - 67  (or portions thereof). As described in more detail elsewhere herein, a thin device may be virtually provisioned in terms of its allocated physical storage in physical storage for a thin device presented to a host as having a particular capacity is allocated as needed rather than allocate physical storage for the entire thin device capacity upon creation of the thin device. As such, a thin device presented to the host as having a capacity with a corresponding LBA (logical block address) range may have portions of the LBA range for which storage is not allocated. 
     Referring to  FIG. 5C , shown is a diagram  150  illustrating tables that are used to keep track of device information. A first table  152  corresponds to all of the devices used by a data storage system or by an element of a data storage system, such as an HA  21  and/or a DA  23 . The table  152  includes a plurality of logical device (logical volume) entries  156 - 158  that correspond to all the logical devices used by the data storage system (or portion of the data storage system). The entries in the table  152  may include information for thin devices, for data devices (such as logical devices or volumes), for standard logical devices, for virtual devices, for BCV devices, and/or any or all other types of logical devices used in connection with the system described herein. 
     Each of the entries  156 - 158  of the table  152  correspond to another table that may contain information for one or more logical volumes, such as thin device logical volumes. For example, the entry  157  may correspond to a thin device table  162 . The thin device table  162  may include a header  164  that contains overhead information, such as information identifying the corresponding thin device, information concerning the last used data device and/or other information including counter information, such as a counter that keeps track of used group entries (described below). The header information, or portions thereof, may be available globally to the data storage system. 
     The thin device table  162  may include one or more group elements  166 - 168 , that contain information corresponding to a group of tracks on the data device. A group of tracks may include one or more tracks, the number of which may be configured as appropriate. In an embodiment herein, each group has sixteen tracks, although this number may be configurable. 
     One of the group elements  166 - 168  (for example, the group element  166 ) of the thin device table  162  may identify a particular one of the data devices  61 - 67  having a track table  172  that contains further information, such as a header  174  having overhead information and a plurality of entries  176 - 178  corresponding to each of the tracks of the particular one of the data devices  61 - 67 . The information in each of the entries  176 - 178  may include a pointer (either direct or indirect) to the physical address on one of the physical disk drives of the data storage system that maps to the logical address(es) of the particular one of the data devices  61 - 67 . Thus, the track table  162  may be used in connection with mapping logical addresses of the logical devices corresponding to the tables  152 ,  162 ,  172  to physical addresses on the disk drives or other physical devices of the data storage system. 
     The tables  152 ,  162 ,  172  may be stored in the global memory  25   b  of the data storage system. In addition, the tables corresponding to particular logical devices accessed by a particular host may be stored (cached) in local memory of the corresponding one of the HA&#39;s. In addition, an RA and/or the DA&#39;s may also use and locally store (cache) portions of the tables  152 ,  162 ,  172 . 
     Referring to  FIG. 5D , shown is a schematic diagram illustrating a group element  166  of the thin device table  162  in connection with an embodiment of the system described herein. The group element  166  may includes a plurality of entries  166   a - 166   f  The entry  166   a  may provide group information, such as a group type that indicates whether there has been physical address space allocated for the group. The entry  166   b  may include information identifying one (or more) of the data devices  61 - 67  that correspond to the group (i.e., the one of the data devices  61 - 67  that contains pointers for physical data for the group). The entry  166   c  may include other identifying information for the one of the data devices  61 - 67 , including a speed indicator that identifies, for example, if the data device is associated with a relatively fast access physical storage (disk drive) or a relatively slow access physical storage (disk drive). Other types of designations of data devices are possible (e.g., relatively expensive or inexpensive). The entry  166   d  may be a pointer to a head of the first allocated track for the one of the data devices  61 - 67  indicated by the data device ID entry  166   b . Alternatively, the entry  166   d  may point to header information of the data device track table  172  immediately prior to the first allocated track. The entry  166   e  may identify a cylinder of a first allocated track for the one the data devices  61 - 67  indicated by the data device ID entry  166   b . The entry  166   f  may contain other information corresponding to the group element  166  and/or the corresponding thin device. In other embodiments, entries of the group table  166  may identify a range of cylinders of the thin device and a corresponding mapping to map cylinder/track identifiers for the thin device to tracks/cylinders of a corresponding data device. In an embodiment, the size of table element  166  may be eight bytes. 
     Accordingly, a thin device presents a logical storage space to one or more applications running on a host where different portions of the logical storage space may or may not have corresponding physical storage space associated therewith. However, the thin device is not mapped directly to physical storage space. Instead, portions of the thin storage device for which physical storage space exists are mapped to data devices, which are logical devices that map logical storage space of the data device to physical storage space on the disk drives or other physical storage devices. Thus, an access of the logical storage space of the thin device results in either a null pointer (or equivalent) indicating that no corresponding physical storage space has yet been allocated, or results in a reference to a data device which in turn references the underlying physical storage space. 
     Thin devices and thin provisioning are described in more detail in U.S. patent application Ser. No. 11/726,831, filed Mar. 23, 2007 (U.S. Patent App. Pub. No. 2009/0070541 A1), AUTOMATED INFORMATION LIFE-CYCLE MANAGEMENT WITH THIN PROVISIONING, Yochai, EMS-147US, and U.S. Pat. No. 7,949,637, Issued May 24, 2011, Storage Management for Fine Grained Tiered Storage with Thin Provisioning, to Burke, both of which are incorporated by reference herein. 
     As discussed elsewhere herein, the data devices  61 - 67  (and other logical devices) may be associated with physical storage areas (e.g., disk drives, tapes, solid state storage, etc.) having different characteristics. In various embodiments, the physical storage areas may include multiple tiers of storage in which each sub-tier of physical storage areas and/or disk drives may be ordered according to different characteristics and/or classes, such as speed, technology and/or cost. The devices  61 - 67  may appear to a host coupled to the storage device  24  as a logical volume (logical device) containing a contiguous block of data storage, as discussed herein. Accordingly, each of the devices  61 - 67  may map to storage areas across multiple physical storage drives. The granularity at which the storage system described herein operates may be smaller than at the file level, for example potentially as small as a single byte, but more practically at the granularity of a single logical block or collection of sequential data blocks. A data block may be of any size including file system or database logical block size, physical block, track or cylinder and/or other size. Multiple data blocks may be substantially the same size or different sizes, such as different size data blocks for different storage volumes or different sized data blocks within a single storage volume. 
     In accordance with techniques herein, an embodiment may allow for locating all of the data of a single logical portion or entity in a same tier or in multiple different tiers depending on the logical data portion or entity. In an embodiment including thin devices, the techniques herein may be used where different portions of data of a single thin device may be located in different storage tiers. For example, a thin device may include two data portions and a first of these two data portions may be identified as a “hot spot” of high I/O activity (e.g., having a large number of I/O accesses such as reads and/or writes per unit of time) relative to the second of these two portions. As such, an embodiment in accordance with techniques herein may have added flexibility in that the first portion of data of the thin device may be located in a different higher performance storage tier than the second portion. For example, the first portion may be located in a tier comprising flash devices and the second portion may be located in a different tier of FC or SATA drives. 
     Referring to  FIG. 6 , shown is an example illustrating information that may be defined and used in connection with techniques herein. The example  200  includes multiple storage tiers  206 ,  208 , and  210 , an allocation policy (AP)  204 , and storage group (SG)  202 . The SG  202  may include one or more thin devices (TDs), such as TD A  220  and TD B  222 , used by an application  230 . The application  230  may execute, for example, on one of the hosts of  FIG. 1 . The techniques herein may be used to determine how to partition physical storage of the multiple storage tiers  206 ,  208  and  210  for use in storing or locating the application&#39;s data, such as data of the TDs  220  and  222 . It should be noted that the particular number of tiers, TDs, and the like, should not be construed as a limitation. An SG may represent a logical grouping of TDs used by a single application although an SG may correspond to other logical groupings for different purposes. An SG may, for example, correspond to TDs used by multiple applications. 
     Each of  206 ,  208  and  210  may correspond to a tier definition as described elsewhere herein. Element  206  represents a first storage tier of flash drives having a tier capacity limit C 1 . Element  208  represents a first storage tier of FC drives having a tier capacity limit C 2 . Element  210  represents a first storage tier of SATA drives having a tier capacity limit C 3 . Each of C 1 , C 2  and C 3  may represent an available or maximum amount of storage capacity in the storage tier that may be physical available in the system. The AP  204  may be associated with one of more SGs such as SG  202 . The AP  204  specifies, for an associated SG  202 , a capacity upper limit or maximum threshold for one or more storage tiers. Each such limit may identify an upper bound regarding an amount of storage that may be allocated for use by the associated SG. The AP  204  may be associated with one or more of the storage tiers  206 ,  208  and  210  that may be defined in a multi-tier storage environment. The AP  204  in this example  200  includes limit  204   a  identifying a maximum or upper limit of storage for tier1, limit  204   b  identifying a maximum or upper limit of storage for tier2, and limit  204   c  identifying a maximum or upper limit of storage for tier3. The SG  202  may be based on an SG definition identifying  202   a  the logical devices, such as TDs included in the SG. 
     In connection with techniques herein, the maximum limits  204   a ,  204   b  and  204   c  each represent an upper bound of a storage capacity to which an associated SG is subjected to. The techniques herein may be used to partition less than the amount or capacity represented by such limits. An amount of physical storage of a tier allocated for use by an application is allowed to vary up to the tier limit as defined in the AP  204  in accordance with other criteria associated with the application such as, for example, varying application workload. The optimizer may vary the amount of storage in each tier used by an SG 202 , and thus an application, based on workload and possibly other criteria when performing a cost benefit analysis, where such amounts are subject to the limits of the SG&#39;s AP and also performance limits described in more detail elsewhere herein. At a second point in time, the workloads and possibly other criteria for the applications may change and the optimizer may reparation the storage capacity used by each application subject to the capacity limits of APs and performance limits. 
     Referring to  FIG. 7 , shown is an example which more generally illustrates different associations between SGs, APs and tiers in an embodiment in accordance with techniques herein. The example  350  illustrates that an embodiment may have multiple storage tiers (e.g., tiers 1-N), multiple APs (e.g, AP 1 -N), and multiple SGs (e.g., SG  1 -M). Each AP may be associated with one or more of the storage tiers. Each AP may also be associated with different tiers than other APs. For example, APn is associated with Tier N but AP 1  is not. For each tier associated with an AP, the AP may define a maximum capacity limit as described in connection with  FIG. 6 . Each AP may be associated with one or more SGs. For example SGs 1 -N may be associated with a same AP 1 , and SGs N+1 through M may be associated with a same APn. 
     With reference back to  FIG. 6 , each of the maximum capacity limits may have any one of a variety of different forms. For example, such limits may be expressed as a percentage or portion of tier total storage capacity (e.g., such as a percentage of C 1 , C 2 , or C 3 ), as an integer indicating an amount or quantity of storage  410   c  (e.g., indicating a number of bytes or other number of storage units), and the like. 
     Data used in connection with techniques herein, such as the performance data of  FIG. 3  used in determining device and SG workloads, may be obtained through observation and monitoring actual performance. Data may also be determined in other suitable ways such as, for example, through simulation, estimation, and the like. Observed or collected data may be obtained as described in connection with  FIG. 3  by monitoring and recording one or more aspects of I/O activity for each TD, and portions thereof. For example, for each TD, and/or portions thereof, an average number of reads occurring within a given time period may be determined, an average number of writes occurring within a given time period may be determined, an average number of read misses occurring within a given time period may be determined, and the like. It should be noted that the operations of read and write with respect to an TD may be viewed as read and write requests or commands from the DA, controller or other backend physical device interface. Thus, these are operations may also be characterized as a average number of operations with respect to the physical storage device (e.g., average number of physical device reads, writes, and the like, based on physical device accesses). This is in contrast to observing or counting a number of particular type of I/O requests (e.g., reads or writes) as issued from the host and received by a front end component such as an FA. To illustrate, a host read request may not result in a read request or command issued to the DA if there is a cache hit and the requested data is in cache. The host read request results in a read request or command issued to the DA to retrieve data from the physical drive only if there is a read miss. Furthermore, when writing data of a received host I/O request to the physical device, the host write request may result in multiple reads and/or writes by the DA in addition to writing out the host or user data of the request. For example, if the data storage system implements a RAID data protection technique, such as RAID-5, additional reads and writes may be performed such as in connection with writing out additional parity information for the user data. Thus, observed data gathered to determine workload, such as observed numbers of reads and writes, may refer to the read and write requests or commands performed by the DA. Such read and write commands may correspond, respectively, to physical device accesses such as disk reads and writes that may result from a host I/O request received by an FA. 
     It should be noted that movement of data between tiers from a source tier to a target tier may include determining free or unused storage device locations within the target tier. In the event there is an insufficient amount of free of unused storage in the target tier, processing may also include displacing or relocating other data currently stored on a physical device of the target tier. An embodiment may perform movement of data to and/or from physical storage devices using any suitable technique. Also, any suitable technique may be used to determine a target storage device in the target tier where the data currently stored on the target is relocated or migrated to another physical device in the same or a different tier. 
     One embodiment in accordance with techniques herein may include multiple storage tiers including a first tier of flash devices and one or more other tiers of non-flash devices having lower performance characteristics than flash devices. The one or more other tiers may include, for example, one or more types of disk devices. The tiers may also include other types of SSDs besides flash devices. 
     As described above, a thin device (also referred to as a virtual provision device) is a device that represents a certain capacity having an associated address range. Storage may be allocated for thin devices in chunks or data portions of a particular size as needed rather than allocate all storage necessary for the thin device&#39;s entire capacity. Therefore, it may be the case that at any point in time, only a small number of portions or chunks of the thin device actually are allocated and consume physical storage on the back end (on physical disks, flash or other physical storage devices). A thin device may be constructed of chunks having a size that may vary with embodiment. For example, in one embodiment, a chunk may correspond to a group of 12 tracks (e.g., 12 tracks*64 Kbytes/track=768 Kbytes/chunk). As also noted with a thin device, the different chunks may reside on different data devices in one or more storage tiers. In one embodiment, as will be described below, a storage tier may consist of one or more storage pools. Each storage pool may include multiple LVs and their associated physical devices. With thin devices, a system in accordance with techniques herein has flexibility to relocate individual chunks as desired to different devices in the same as well as different pools or storage tiers. For example, a system may relocate a chunk from a flash storage pool to a SATA storage pool. In one embodiment using techniques herein, a thin device can be bound to a particular storage pool of a storage tier at a point in time so that any chunks requiring allocation of additional storage, such as may occur when writing data to the thin device, result in allocating storage from this storage pool. Such binding may change over time for a thin device. 
     A thin device may contain thousands and even hundreds of thousands of such chunks. As such, tracking and managing performance data such as one or more performance statistics for each chunk, across all such chunks, for a storage group of thin devices can be cumbersome and consume an excessive amount of resources. Described in following paragraphs are techniques that may be used in connection with collecting performance data about thin devices where such information may be used to determine which chunks of thin devices are most active relative to others. Such evaluation may be performed in connection with determining promotion/demotion thresholds use in evaluating where to locate and/or move data of the different chunks with respect to the different storage tiers in a multi-storage tier environment. In connection with examples in following paragraphs, details such as having a single storage pool in each storage tier, a single storage group, and the like, are provided for purposes of illustration. Those of ordinary skill in the art will readily appreciate the more general applicability of techniques herein in other embodiments such as, for example, having a storage group include a plurality of storage pools, and the like. 
     Referring to  FIG. 8A , shown is an example  700  illustrating use of a thin device in an embodiment in accordance with techniques herein. The example  700  includes three storage pools  712 ,  714  and  716  with each such pool representing a storage pool of a different storage tier. For example, pool  712  may represent a storage pool of tier A of flash storage devices, pool  714  may represent a storage pool of tier B of FC storage devices, and pool  716  may represent a storage pool of tier C of SATA storage devices. Each storage pool may include a plurality of logical devices and associated physical devices (or portions thereof) to which the logical devices are mapped. Element  702  represents the thin device address space or range including chunks which are mapped to different storage pools. For example, element  702   a  denotes a chunk C 1  which is mapped to storage pool  712  and element  702   b  denotes a chunk C 2  which is mapped to storage pool  714 . Element  702  may be a representation for a first thin device which is included in a storage group of one or more thin devices. 
     It should be noted that although the example  700  illustrates only a single storage pool per storage tier, an embodiment may also have multiple storage pools per tier. 
     Referring to  FIG. 8B , shown is an example representation of information that may be included in an allocation map in an embodiment in accordance with techniques herein. An allocation map may be used to identify the mapping for each thin device (TD) chunk (e.g. where each chunk is physically located). Element  760  represents an allocation map that may be maintained for each TD. In this example, element  760  represents information as may be maintained for a single TD although another allocation map may be similarly used and maintained for each other TD in a storage group. Element  760  may represent mapping information as illustrated in  FIG. 8A  such as in connection the mapping of  702  to different storage pool devices. The allocation map  760  may contain an entry for each chunk and identify which LV and associated physical storage is mapped to the chunk. For each entry or row of the map  760  corresponding to a chunk, a first column  760   a , Chunk ID, denotes an identifier to uniquely identify the chunk of the TD, a second column  760   b , indicates information about the LV and offset to which the chunk is mapped, and a third column storage pool  760   c  denotes the storage pool and tier including the LV of  760   b . For example, entry  762  represents chunk C 1  illustrated in  FIG. 8A  as  702   a  and entry  764  represents chunk C 2  illustrated in  FIG. 8A  as  702   b . It should be noted that although not illustrated, the allocation map may include or otherwise use other tables and structures which identify a further mapping for each LV such as which physical device locations map to which LVs. This further mapping for each LV is described and illustrated elsewhere herein such as, for example, with reference back to  FIG. 5B . Such information as illustrated and described in connection with  FIG. 8B  may be maintained for each thin device in an embodiment in accordance with techniques herein. 
     In connection with collecting statistics characterizing performance, workload and/or activity for a thin device, one approach may be to collect the information per chunk or, more generally, for the smallest level of granularity associated with allocation and deallocation of storage for a thin device. Such statistics may include, for example, a number of reads/unit of time, #writes/unit of time, a number of prefetches/unit of time, and the like. However, collecting such information at the smallest granularity level does not scale upward as number of chunks grows large such as for a single thin device which can have up to, for example 300,000 chunks. 
     Therefore, an embodiment in accordance with techniques herein may collect statistics on a grouping of “N” chunks also referred to as an extent, where N represents an integer number of chunks, N&gt;0. N may be, for example, 480 in one embodiment. Each extent may represent a consecutive range or portion of the thin device in terms of thin device locations (e.g., portion of the address space or range of the thin device). Note that the foregoing use of consecutive does not refer to physical storage locations on physical drives but rather refers to consecutive addresses with respect to a range of addresses of the thin device which are then mapped to physical device locations which may or may not be consecutive, may be on the same or different physical drives, and the like. For example, in one embodiment, an extent may be 480 chunks (N=480) having a size of 360 MBs (megabytes). 
     An extent may be further divided into sub extents, where each sub extent is a collection of M chunks. M may be, for example  10  in one embodiment. In one embodiment, the sub-extent size may correspond to the smallest granularity of data movement. In other words, the sub extent size represents the atomic unit or minimum amount of data that can be operated upon when performing a data movement such as between storage tiers. 
     Referring to  FIG. 9 , shown is an example illustrating partitioning of a thin device&#39;s address space or range in an embodiment in accordance with techniques herein. The example  250  includes a thin device address space or range  252  which, as described elsewhere herein, includes chunks mapped to physical storage locations. The thin device address space or range  252  may be partitioned into one or more extents  254   a - 254   n . Each of the extents  254   a - 254   n  may be further partitioned into sub-extents. Element  260  illustrates that extent X  254   n  may include sub extents  256   a - 256   n . Although only detail is illustrated for extent  254   n , each of the other extents of the thin device also include a same number of sub extents as illustrated for  254   n . Each of the sub extents  256   a - 256   n  may represent a grouping of “M” chunks. Element  262  illustrates that sub extent 1  256   a  may include chunks  258   a - 258   n . Although only detail is illustrated for sub extent  256   a , each of the other sub extents  256   b - 256   n  also include a same number of “M” chunks as illustrated for  256   a . Thus, each of the extents  254   a - 254   n  may represent an grouping of “N” chunks, where
 
 N =#sub extents/extent* M  chunks/sub extent  EQUATION 1
 
     An embodiment in accordance with techniques herein may collect statistics for each extent and also other information characterizing activity of each sub extent of a thin device. Statistics for each extent may be characterized as either long term or short term. Short term refers to statistics which may reflect performance, workload, and/or I/O activity of an extent with respect to a relatively short window of time. Thus, short term statistics may reflect recent extent activity for such a short time period. In contrast and relative to short term, long term refers to statistics reflecting performance, workload and/or I/O activity of an extent with respect to a longer period of time. Depending on the evaluation being performed, such as by the optimizer, it may be desirable to place greater weight on short term information than long term, or vice versa. Furthermore, the information maintained per sub extent may be used as needed once particular extents of interest have been identified. 
     Referring to  FIG. 10 , shown is an example of information that may be collected and used in connection each extent in an embodiment in accordance with techniques herein. The example  300  illustrates that short term information  302 , long term information  304  and a sub extent activity bitmap  306  may be collected for each extent. The short term information  302  and long term information  304  may be used in connection with determining short term rates  320  and long term rates  330  for each extent. The statistics included in  302 ,  304 ,  320  and  330  may reflect activity with respect to the entire extent. The activity bitmap  306  is illustrated in further detail by element  307  as including an entry for each sub extent in the associated extent. Entries of  307  are denoted by A, B, C, and the like. Each of the entries of  307  represents aggregated or collective activity information for a corresponding sub extent denoted by the numeric identifiers  307   a  of 1, 2, 3, etc. Each entry of  307  may include one or more bits used to encode an activity level with respect to all chunks of a corresponding sub-extent. For example, the entry of  307  denoted as A represents an activity level for all chunks in sub extent 1. An embodiment may use any number of bits for each entry of the activity bitmap  306 ,  307 . For example, in one embodiment, each entry of the activity bitmap may be 2 bits capable of representing any of 4 integer values—0, 1, 2, and 3. 
     As will be described in following paragraphs, the short term rates  320 , long term rates  330  and sub extent activity bitmap  306  may be used in connection with a variety of different evaluations such as by the optimizer  138 . Generally, the activity level information or data for an extent such as illustrated in  FIG. 10  may be referred to as extent activity level information including one or more metrics indicating an activity level for the extent. The extent activity level information may comprise short term activity information (e.g., such as  302  and/or  320 ) and long term activity information (e.g., such as  304  and  330 ). 
     In one embodiment, the short term rates  320  for an extent may include a read miss rate (e.g., random read miss (RRM) rate)  322 , a write I/O rate  324  and a prefetch rate  326  for the extent. The long term rates  330  for an extent may include a read miss rate  332  (e.g., number of read misses/unit of time, where a read miss refers to a cache miss for a read), a write I/O rate  334  (e.g., number of writes/unit of time) and a prefetch rate  336  (e.g., number of prefetches/unit of time) for the extent. As known in the art, data may be prefetched from a physical device and placed in cache prior to reference or use with an I/O operation. For example, an embodiment may perform sequential stream I/O recognition processing to determine when consecutive portions of a thin device are being referenced. In this case, data of the sequential stream may be prefetched from the physical device and placed in cache prior to usage in connection with a subsequent I/O operation. In connection with a portion of data at a first point in a sequential stream associated with a current I/O operation, data subsequent to the first point may be prefetched such as when obtaining the portion from a physical device in anticipation of future usage with subsequent I/Os. The short term prefetch rate  326 , as well as the long term prefetch rate  336 , may also be referred to as denoting a number of sequential reads or sequential read miss operations performed since such prefetching may occur in response to determination that a read operation is performed for data which is not in cache (read miss) and the read operation is for data included in a series of sequentially read data portions as described above. The read miss rates  322  and  332  may represent random read miss (RRM) rates where such read misses (e.g., data requested not currently in cache) are associate with read operations not included in connection with reading data of a sequential stream (e.g., all read misses not used in connection with computing  326  and  336 ). 
     Each of the foregoing rates of  320  and  330  may be with respect to any unit of time, such as per second, per hour, and the like. In connection with describing elements  302  and  304  in more detail, what will be described is how an embodiment in accordance with techniques herein may determine the short term rates  320  and long term rates  330  using a decay function and decay coefficients. 
     In an embodiment in accordance with techniques herein, a decay coefficient may be characterized as a weighting factor given to previous activity information. The higher the coefficient, the greater the weight given to previous activity information for the extent. Thus, the adjusted activity level of an extent at a current time, “An”, may be generally represented as a function of a current observed or actual activity level for the current time, “a n ”, a decay coefficient, “r”, and previous adjusted activity level for the previous time period or sampling period, “A n-1 ”. In connection with the foregoing, “A” may represent an adjusted activity level, “n” may denote the current time period or sampling period and “n−1” may denote the immediately prior or previous time period or sampling period at which the activity for the extent was determined. In other words, “a n ” is adjusted to take into account previous activity as represented by “A n-1 ” and “An” represents the resulting adjusted value of “a n ”. With respect to a statistic or metric such as a number or read misses, “a n ” and “An” may each represent an integer quantity or number of read misses within a current sampling period, “n”. The foregoing may generally be represented as:
 
 An=a   n   +*A   n-1 )  EQUATION 2
 
wherein
 
     a n  is the actual observed activity metric for the current or “nth” sampling period, 
     “r” is a decay coefficient, 
     “A n ” is the adjusted activity metric for the current or “nth” sampling period, and 
     “A n-1 ” is the adjusted activity metric from the previous or “n−1” sampling period. 
     Beginning with an initial time period or sampling period, denoted by i=“0” (zero), the adjusted activity A 0  may be initially that which is observed, a 0 . Subsequent observed or actual activity levels may be adjusted as described above. Generally, “a i ” may denote an actual or observed value obtained for an activity metric for a sampling period “i”, where “i” is an integer greater than or equal to 0. “Ai” may similarly denote an adjusted activity metric (or adjusted value for “a i ”) for a sampling period “i”, where “i” is an integer greater than or equal to 0. Thus, for consecutive sample periods at which actual or observed activity metrics are obtained (as denoted by lower case “a i ”s), corresponding adjusted activity levels (e.g., “A” values) may be determined as follows:
 
 A 0= a 0/*Adjusted activity level  A 0, at time=0 or initially*/
 
 A 1= a 1+( r*A 0)/*Adjusted activity level  A 1, at first sampling period, i= 1*/
 
 A 2= a 2+( r*A 1)/*Adjusted activity level  A 2, at second sampling period, i= 2*/and
 
so on for subsequent sampling periods 3, 4, and the like, based on EQUATION 2.
 
     In connection with EQUATION 2, 0&lt;=r&lt;1, where “r” is a decay coefficient or weight given to previous activity. Varying “r” in EQUATION 2 results in accordingly varying the weight given to past or previous activity. If r=0, then no weight is given to previous or historic values. Thus, the closer “r” is to 0, the lesser weight given to previous activity. Similarly, the closer “r” is to 1, the greater the weight given to previous activity. In connection with determining an adjusted activity level, An, using EQUATION 2 for short term and long term, different decay coefficients may be selected. Generally “r” for short term is less than “r” used in connection with long term activity. For example, in one embodiment, “r” used in connection short term activity levels may be 50% or 0.50 or smaller. “r” used in connection with long term activity levels may be 80% or 0.80 or larger. The foregoing are exemplary values that may be selected for “r” in connection with short term and long term activity levels depending on the weight to be given to previous activity. In connection with short term activity, a decay coefficient may be selected in accordance with providing a relatively short term rate of decay for an activity level metric determined at a point in time. For example, a short term rate of decay may provide for a rate of decay for an activity level metric on the order of one or more hours (e.g., less than a day). In connection with long term activity, a decay coefficient may be selected in accordance with providing a relatively long term rate of decay for an activity level metric determined at a point in time. For example, a long term rate of decay may provide for a rate of decay for an activity level metric on the order of one or more days, a week, and the like. Thus, an activity metric at a first point in time may have a weighted or residual effect on an adjusted activity level determined at a later point in time in accordance with the selected decay coefficient indicating the rate of decay of the activity metric. 
     As mentioned above, EQUATION 2 results in a metric or count, such as a number of read misses, number of writes, or number or prefetches during a sample period. It may be desirable to also determine a rate with respect to a unit of time, such as per second, per hour, and the like, for each of the foregoing adjusted activity metrics An. A rate with respect to a unit of time for the adjusted activity level An may be represented as:
 
 Ar=An *(1− r )/(1− r   n-1 )  EQUATION 3
 
where Ar=the adjusted activity rate per unit of time,
 
     r=decay coefficient or weight as described above, 
     n=denotes an “nth” sampling period as described above, 
     An=adjusted activity level determined for a given sampling period “n” (e.g. using EQUATION 2 as described above). 
     Generally, the higher the decay coefficient, r, the slower the change in Ar as may be the desired case with long term Ar values. Thus an embodiment may select decay coefficients for use with long term and short term Ar values so that, when plotted with respect to time, long term Ar values generally have a smaller slope than that associated with short term Ar values. 
     Referring to  FIG. 11 , shown is an example graphically illustrating the general shape of curves for long term (LT) and short term (ST) values in an embodiment in accordance with techniques herein. The activity level values (Y-axis values) are plotted with respect to time (X-axis). The activity level values may be determined using EQUATIONS 2 and/or 3. Curve  402  may be produced using one of EQUATIONS 2 and 3 where a first value for the decay coefficient “r” is selected for ST usage. Curve  404  may be produced using one of EQUATIONS 2 and 3 where a second value for the decay coefficient “r” is selected for LT usage. The values selected for “r” in connection with  402  and  404  may be relative so that the first value for “r” used with  402  is less than the second value for “r” used with  404 . 
     In one embodiment, each of the different An values determined using EQUATION 2 may be converted to a corresponding Ar value using EQUATION 3 when desired. 
     In connection with the foregoing, for example, with respect to a number of read misses, “a n ” represents the number of such operations that have occurred in a current sample period, n. For example, if a sample period=10 minutes so that statistics for an extent are collected and/or computed every 10 minutes, “a n ” represents the number of read misses that occurred in the last 10 minute sample period or time interval. A n-1  represents the previous or last A calculation (e.g., as determined using EQUATION 2) from the previous sample period, denoted “n−1”. 
     With reference back to  FIG. 10 , an embodiment may collect short term information  302  as counter values indicating a count or number of each type of operation for a current time period or sampling period “n”. The following may represent different “a n ” values as included in the short term information  302  for an extent: read miss count (number of read misses for the extent during the sampling period), prefetch count (number of prefetches for the extent during the sampling period) and write count (number of writes for the extent during the sampling period). 
     The short term information  302  may also include storing previous A values as determined for the sampling period “n−1” using EQUATION 2 above. For example, short term information  302  may also include storing three (3) previous adjusted activity level values or A values for sampling period “n−1” using EQUATION 2 above for the read miss count, prefetch count and write count. 
     The short term information  302  may also include a timestamp value indicating the timestamp associated with the previous sampling period “n−1”. 
     Using the above-mentioned short term information  302 , an embodiment may calculate updated short term rates  320  using EQUATION 3 for a sampling period “n” for a selected “r” as a short term decay coefficient. With each new sampling period, the short term information may be accordingly updated so that which is associated with sampling period “n” subsequently becomes associated with sampling period “n−1”. 
     The long term information  304  may include long term rates or Ar values as determined using EQUATION 3 for a read miss rate (e.g., number of read misses/second), a prefetch rate (e.g., number of prefetches/second) and a write rate (e.g., number of writes/second). The long term information  304  may also include a time duration interval used for determining an adjusted Ar value for the current time or sampling period “n”. For example, the time duration interval may represent the amount of time for which statistics are collected and used in connection with long term Ar values. An embodiment may store a set of long term Ar values rather than calculate such Ar values on demand from other stored information as in the case above for short term rates  320  (e.g., where short term information  302  is stored and used to calculate short term rates  320  on demand). Thus, in such an embodiment, the long term rates  330  may be included the long term information  304  where such long term rates  330  may be updated with each sampling period. In one embodiment with the arrival of a new sampling period “n”, the long term information  304  may include Ar values for the foregoing statistics as determined using EQUATION 3 for a sampling period “n−1”. These long term Ar values for “n−1” may each be multiplied by the time duration interval to determine A n-1 , an adjusted metric for the long term time period. The foregoing A n-1  value may then be used with EQUATION 2 to determine An for the current sampling period “n” using a selected “r” as a long term decay coefficient. Using An, EQUATION 3 may then be used to obtain updated long term rates Ar values. With each new sampling period, the long term information may be accordingly updated so that which is associated with sampling period “n” subsequently becomes associated with sampling period “n−1”. 
     With reference back to  FIG. 10 , described above is an activity bitmap  306  having an entry per sub extent where each such entry may indicate an aggregate or collective activity level with respect to all chunks of the associated sub-extent. The number of different activity level states that may be represented for each sub extent depends on the number of bits per entry of the activity bitmap. In one embodiment, each entry of the activity bitmap may be 2 bits as described above so that each entry may be an integer in the inclusive range of 0 . . . 3. Processing may be performed to decrement each entry having a non-zero value by 1 every predetermined time period, such as every 12 hours. Each time there is any I/O operation to a sub extent since the sub extent was located or moved to its current physical location, the sub extent&#39;s entry in the activity bitmap  306  may be set to 3. Thus, each entry in the bitmap may represent activity level information for up to 3 of the predetermined 12 hour time periods. An embodiment may also have a different number of bits per entry to represent a larger number of predetermined time periods. Based on the foregoing, the lower the value of a bitmap entry for a sub extent, the longer the amount of time that has lapsed since the sub extent has had any I/O activity. 
     Referring to  FIG. 12 , shown is a flowchart of processing steps that may be performed in connection with each activity bitmap associated with an extent in an embodiment in accordance with techniques herein. The flowchart  500  summarizes processing described above where each bitmap for each extent may be traversed with the occurrence of a predetermined time interval, such as every 12 hours. At step  502 , a determination is made as to whether the next time interval has lapsed. If not, processing waits at step  502  until step  502  evaluates to yes and control proceeds to step  504 . At step  504 , I is initialized to the next entry in the bitmap. I represents a loop counter when traversing through the bitmap and denotes the bitmap entry currently selected for processing. At step  506 , a determination is made as to whether the entire bitmap has been processed. If step  506  evaluates to yes, control proceeds to step  502  until an amount of time again lapses equal to that of the time interval. If step  506  evaluates to no, control proceeds to step  508  where a determination is made as to whether the current bitmap entry (e.g. bitmap [I]) is zero. If so, control proceeds to step  504 . Otherwise, control proceeds to step  510  where the current bit map entry is decremented by one (1) and control proceeds to step  504  to process the next entry in the bitmap. 
     The activity bitmap may be used in connection with determining an activity level associated with each sub extent, the smallest amount of data that can be associated with a data movement operation to relocate data from one physical device to another. It should be noted that an embodiment may have functionality and capability to physically move data in units or amounts less than a sub extent. However, when performing processing to determine data movement candidates, such as by the optimizer, such processing may consider candidates for data movement which have a minimum size of a sub extent. That is, all data of the sub extent may be either moved or relocated as a complete unit, or remains in its current location. In connection with a sub extent when performing a data movement, it may be that not all chunks of the sub extent are actually moved. For example, suppose a sub extent is 10 chunks and the sub extent is to be moved from a first storage tier, such as from SATA or FC, to a second storage tier, such as flash. It may be that 9/10 chunks of the sub extent are unallocated or already in flash storage with only 1 chunk stored in the first storage tier. In this case, processing only needs to actually move the single chunk from the first storage tier to flash since the remaining 9 chunks are either already in the flash tier or unallocated. With a sub extent, the amount of data actually moved may be at most the size of the sub extent but may be less depending on, for example, whether all chunks of the thin device sub extent are allocated (e.g., actually map to physical storage), depending on the current physical device upon which chunks of the sub extent are located prior to movement, and the like. It should be noted that chunks of a sub extent may be located in different storage tiers, for example, depending on where the chunk&#39;s data is stored such as at the time when written as well as the result of other conditions that may vary with embodiment. 
     As an example use of the activity bitmap is in connection with promotion and demotion. As an example use of the activity bitmap, the bitmap may be used to determine selective sub extents which exhibit the highest activity level such as those having counters=3 (e.g., “hot” or active areas of the extent). These sub extents may be candidates for promotion or data movement to a higher performing storage tier and may be given preference for such promotion and data movement over other sub extents having activity bitmap entries which are less than 3. In a similar manner, the activity bitmap may be used to identify the “coldest” or inactive sub extents. For example, sub extents having bit map entries=0 may be candidates for demotion to a lower performing storage tier. 
     In connection with promotion data movements, an embodiment may want to be responsive to a change in workload with respect to the short term. With demotion, an embodiment may not want to move data as quickly as with promotion and may also want to consider longer term workloads prior to moving such data to a lesser performing storage tier. With promotion, an embodiment may give greater weight to ST workload and activity data. With demotion, an embodiment may additionally consider LT workload and activity rather than just such ST information. 
     The information as described and illustrated in  FIGS. 10-12  above may be used for a variety of different purposes and evaluations. For example, an embodiment may use one or more of the short term rates to identify one or more active extents based on such aggregated extent-level activity data. Subsequently, once an active extent is identified such as a candidate for promotion, the extent&#39;s activity bitmap may be examined to determine which sub extents are most active. Processing may be performed to selectively move some of the sub extents of the active extent (e.g., those with counters=3) to a higher performing storage tier. 
     As another example, the activity bitmaps of extents may be used to determine a promotion ranking used to identify which extent may be promoted prior to one or more other extents. To further illustrate, an embodiment may have two extents, both which are candidates for promotion. The two extents may exhibit similar activity levels based on aggregate extent-level information such as based on short term rates  320  for each extent. The extent having the lesser number of active sub extents may have a higher priority for movement than the other extent. For example, processing may be performed to count the number of non-zero bit map entries for each of the two extents. The extent having the lower count may have a higher priority than the other extent having a higher count. In other words, the extents may be ranked or ordered for promotion based on a number or count of non-zero bit map entries. The extent having the lower count may be characterized as also exhibiting the greatest activity level density based on the foregoing counts of the activity bitmaps. 
     As another example in connection with demotion, an embodiment may use one or more of the short term rates  320  in combination with one or more of the long term rates  330  to identify one or more inactive extents based on such aggregated extent-level activity data. Subsequently, once an inactive extent is identified, the extent&#39;s activity bitmap may be examined to determine which sub extents are inactive and should be demoted rather than automatically demoting all sub extents of the inactive extent. Processing may be performed to selectively move some of the sub extents (e.g., those with counters=0, counters less than some threshold such as 1, and the like) to a lower performing storage tier. 
     One embodiment in accordance with techniques herein may include multiple storage tiers including a first tier of flash devices and one or more other tiers of non-flash devices having lower performance characteristics than flash devices. The one or more other tiers may include, for example, one or more types of disk devices. The tiers may also include other types of SSDs besides flash devices. 
     The different levels of activity information described herein as obtained at a thin device level, extent level, and sub extent level provide a hierarchical view for characterizing activity of different portions of thin devices. Activity information at higher device levels may be used to first identify devices which may be candidates for data movement, such as between storage tiers (e.g. for promotion and/or demotion). In connection with thin devices, once such a first device is identified, additional detail regarding the first device&#39;s activity as reflected in extent activity level information may be used to identify an extent of the first device as a candidate for data movement. Subsequently, the activity bitmap for the extent identified may then be used to determine one or more sub extents of the identified extent for data movement. The techniques herein may be used for collecting and tracking activity of thin devices. Use of the decay coefficients and equations for determining adjusted activity levels to account for previous activity levels provides an effective way of tracking workload and activity over time without having to keep a large database of historical statistics and metrics for long and short time periods. 
     In addition to the activity information described above for each extent and sub extent of a thin device, an embodiment may also track device level activity information for logical devices (e.g., thin devices, LVs, and the like) and physical devices in a data storage system as also noted. Additionally, an embodiment may track activity information for thin device pools. When a DA or other device interface services an I/O, the DA may not typically have any knowledge regarding thin devices as may be known from the host&#39;s point of view. In connection with collecting data for use with techniques herein, each DA may be provided with additional mapping information regarding thin devices and where storage for the thin devices is allocated (e.g., such as described by the allocation map). The DA may use this information to determine what thin device (if any) is associated with a given back end I/O request. When the DA is servicing a back end I/O request, the DA may record information about the I/O including information about the thin device associated with the I/O request. Such additional information about the thin device may be used in order to perform statistics collection of activity data for the thin devices in accordance with techniques herein. 
     In addition to the statistics and activity data described above, an embodiment may also collect and store information regarding expected I/O size information for each extent, thin device (or other logical device), physical device, and the like. Such information may be determined in any one or more suitable ways in an embodiment. For example, an embodiment may determine expected I/O sizes that represent the average size with respect each of the particular types of I/O operations for which statistics are collected. In connection with the embodiment herein, the types of I/O operations for which statistics are collected may be as described above for read miss or random read miss (RRM), prefetch (P) or sequential read miss (SRM), and write (W). In a manner similar to that as described elsewhere herein for other statistics, the average I/O sizes may be determined based on size information collected for observed I/O operations. The collected size information based on observed I/Os may be used in determining or modeling expected I/O sizes in connection with equations, such as EQUATION 4, described elsewhere herein when determining various scores. For example, an embodiment may determine a first average I/O size based on I/O sizes of write operations observed for a time period, a second average I/O size based on I/O sizes for SRM operations for a time period, and a third average I/O size based on I/O sizes for RRM operations for a time period. The foregoing average I/O sizes may be tracked with respect to each extent and other levels (e.g., thin device, physical device, etc) in a manner similar to that as described above such as in  FIG. 10  for other statistics. An embodiment may also use other approaches which may be further simplified. For example, rather than track such I/O size information for each extent, an embodiment may determine an average I/O size with respect to each particular type of I/O operation (W, RRM and SRM) as an aggregate across one or more devices, such as for a physical device, pool of physical devices, thin device, and the like, and then determine an average I/O size with respect to all extents or data portions thereof. In one embodiment, the expected I/O size for any desired I/O operation type such as used in connection with EQUATIONs 4 and 5 described elsewhere herein, may be computed as an average I/O size based on previously gathered data including metrics related to total amount of data (in bytes, megabytes, or other size unit) for a given time period and total number of I/O operations (for the time period over which the total amount of data is determined). More formally, the average I/O size used as an expected I/O size for a given I/O type may be represented as:
 
Ave size for given I/O type=TOTAL_DATA_TRANSFER/TOTAL_OPS  EQUATION 3A
 
where
 
     “Ave size for given I/O type” is the average or expected I/O size for a given I/O operation type (e.g., Read, Write, Read miss, etc.); 
     “TOTAL_DATA_TRANSFER” is the total amount of data (e.g., in bytes, megabytes or other size unit) for the desired I/O operation type for a given time period; and 
     “TOTAL_OPS” is the total number of I/O operations observed during the time period for which the TOTAL_DATA_TRANSFER is observed. 
     It should be noted that EQUATION 3A is one way in which an embodiment may estimate that averages as may be used in connection with expected I/O sizes as described elsewhere herein. Another way an embodiment may determined average I/O sizes is based on a an equation using weighted averages, using information as may be gathered using the allocation map as described elsewhere herein (e.g., to gather information for data portions based on I/Os directed to the physical device where such data portions are stored), and more generally any suitable technique. 
     In connection with techniques in following paragraphs, the extent-based short term and long term statistics or metrics as described in  FIG. 10  may be used in determining scores indicating the activity of extents. In one embodiment, the score may be a weighted value based on a combination of all six metrics  322 ,  324 ,  326 ,  332 ,  334  and  336  of  FIG. 10  although an embodiment may generally use any metrics in determining such scores. In an embodiment herein, a promotion score for an extent may be represented in EQUATION 4 as:
 
(( P 1* P 7* s _ rrm )+( P 2* P 8* s _ w )+( P 3* P 9* s _ p )+( P 4* P 10* l _ rrm )+( P 5* P 11* l _ w )+( P 6* P 12* l _))/(#Active Subext+1)
 
where s_rrm is the rate of short term random read misses ( 322 ), s_w is the rate of short term writes ( 324 ), s_p is the rate of short term pre-fetches or SRMs ( 326 ), l_rrm is the rate of long term random read misses ( 332 ), l_w is the rate of long term writes ( 334 ), and  1   p  is the rate of long term pre-fetches or SRMs. The coefficients P 1 -P 12  may be set as appropriate and are described in more detail elsewhere herein. It should be noted that “#Active Subext” represents the number of active subextents or subportions of an extent or other larger data portion for which the score is being determined. Examples of evaluating when a subextent or other subportion is active are described elsewhere herein. It should be noted that metrics used in connection with determining promotion and/or demotion score may take into account I/O size.
 
     The coefficients P 1 -P 6  may represent weights assigned to the different operation types denoting how much weight is given to each particular operation type (e.g., which of random read miss (RRM), prefetch (P) or sequential read miss (SRM), write (W)) and the long term and short term variants of expected rates at which such operation types are expected (e.g., predicted or modeled) to occur in the future. In one aspect, the coefficients P 1  and P 4  represent the weighted preference given to the RRM operation type relative to the other operations types of SRM (or P) and W. In particular, P 1  represents the weighted preference given to the short term operation count or rate for the RRM operation type and P 4  represents the weighted preference given to the long term operation count or rate for the RRM operation type. In a similar manner, the coefficients P 2  and P 5  represent the weighted preference given to the W operation type relative to the other operations types of SRM (or P) and RRM. In particular, P 2  represents the weighted preference given to the short term operation count or rate for the W operation type and P 5  represents the weighted preference given to the long term operation count or rate for the W operation type. Furthermore, the coefficients P 3  and P 6  represent the weighted preference given to the P or SRM operation type relative to the other operations types of W and RRM. In particular, P 3  represents the weighted preference given to the short term operation count or rate for the P or SRM operation type and P 6  represents the weighted preference given to the long term operation count or rate for the P or SRM operation type. The weights or coefficients P 1 -P 6  may be generally referred to as operation type weights. In some embodiments, values for P 1 -P 6  may be dynamically selected each time a new set of statistics or metrics (e.g.,  320  and  330  of  FIG. 10 ) are utilized in performing processing described herein. Values for P 1 -P 6  may be generally selected based on expected storage tier workload characteristics and particular performance characteristics of physical drives in the different tiers. Examples of how values for P 1 -P 6  may be selected are described in more detail elsewhere herein. It should be noted that an embodiment may also use a combination of fixed values for P 1 -P 6  when determining a score in connection with evaluating which data portions to store in one or more of the storage tiers and may use dynamically determined values for P 1 -P 6  when determining a score in connection with evaluating which data portions to store in one or more others of the storage tiers. For example, an embodiment may use dynamically selected values for P 1 -P 6  when determining a promotion score of EQUATION 4 for use when evaluating which data portions to store in a target high performing SSD or flash-based storage tier, and may otherwise use a same set of fixed values for P 1 -P 6  when determining a promotion score of EQUATION 4 for use when evaluating which data portions to store in a non-SSD or non-flash-based storage tiers (e.g., storage tiers comprising rotational disk drives). In an embodiment, the fixed values used for P 1 -P 6  for non-SSD-based tiers may be 12, 4, 4, 3, 1, and 1, respectively. Of course, different values may be used to emphasize or deemphasize different I/O characteristics in connection with determination of the promotion raw score. Thus, different values for weights P 1 -P 6  may be selected for use depending on which target tier the promotion score is being calculated for. Such values may be dynamically and continually determined based on current expected workload characteristics of a storage tier at different points in time. An embodiment may also selected weights for P 1 -P 6  which are fixed or static throughout operation and performance of the techniques herein where such a fixed set of the same weights may be used for one or more storage tiers. 
     The coefficients P 7 -P 12  may represent additional weights assigned or associated with each particular variant combination of operation type (e.g., RRM, SRM or P, and W) and short term or long term for each operation type. Generally, the coefficients P 7 -P 12  may represent weighting factors that may be characterized as varying with, dependent on, or a function of, expected I/O size for the three different operation types of RRM, SRM or P, and W. In particular, P 7  and P 10  represent weighting factors that vary with, or are a function of, expected I/O size for the RRM operation type. P 8  and P 11  represent weighting factors that vary with, or are a function of, expected I/O size for the W operation type. P 9  and P 12  represent weighting factors that vary with, or are a function of, expected I/O size for the P or SRM operation type. Weights P 7 -P 12  may also be referred to herein as I/O size or data transfer weights. As noted above, EQUATION 3A is one way in which the expected I/O size may be determined for use in connection with determining such weights. It should also be noted that as a variation to the above where in one embodiment, size weights as represented using P 7 -P 12  may be applied to only the short term metrics (e.g., always use a size weight of 1 for weights P 10 , P 11  and P 12  for long term metrics). 
     In some embodiments, values for P 7 -P 12  may be dynamically selected each time a new set of statistics or metrics (e.g.,  320  and  330  of  FIG. 10 ) are utilized in performing processing described herein. Values for P 7 -P 12  may be generally selected based on expected storage tier workload characteristics and particular performance characteristics of physical drives in the different tiers. For example, as described in more detail elsewhere herein, if particular storage tiers have physical drives where a response time or other measurement of performance does not exhibit a substantial dependency upon I/O size, then values for P 7 -P 12  may be 1 so as not to introduce any bias based upon expected I/O sizes for the different operation types. Examples of how values for P 7 -P 12  may be selected are described in more detail elsewhere herein. It should be noted that an embodiment may also use fixed values for P 7 -P 12  when determining a score in connection with evaluating which data portions to store in one or more of the storage tiers and may use dynamically determined values for P 7 -P 12  when determining a score in connection with evaluating which data portions to store in one or more others of the storage tiers. For example, an embodiment may use dynamically selected values for P 7 -P 12  when determining a promotion score of EQUATION 4 for use when evaluating which data portions to store in a target high performing SSD or flash-based storage tier and may otherwise use a set of fixed values for P 7 -P 12  of 1 for all of P 7 -P 12  when determining a promotion score of EQUATION 4 for use when evaluating which data portions to store in a non-SSD or non-flash-based storage tiers (e.g., storage tiers comprising rotational disk drives). Of course, different values may be used to emphasize or deemphasize different I/O characteristics in connection with determination of the promotion raw score. Thus, different values for weights P 7 -P 12  may be selected for use depending on which target tier the promotion score is being calculated for. 
     Values of P 7 -P 12  may be selected as a function of expected I/O sizes for the different operation types. For example, P 7  and P 10  may be determined as a function of the expected I/O size of the RRM operations with respect to the extents for which the promotion score is being determined. P 8  and P 11  may be determined as a function of the expected I/O size of the W operations with respect to the extents for which the promotion score is being determined. P 9  and P 12  may be determined as a function of the expected I/O size of the SRM or P operations with respect to the extents for which the promotion score is being determined. 
     Values for P 7 -P 12  may be based on each storage tier and drive technology depending on the sensitivity of response time to I/O size. Thus, the drive technology may be used in selecting that particular values for P 7 -P 12  such as using curves of  FIGS. 13D and 13E  where the more sensitive or greater dependency between response time and I/O size, the greater the variation in values of the bias or weights assigned. 
     The demotion score for an extent may be represented in EQUATION 5 as:
 
( P 4* P 10* s _ rrm )+( P 5* P 11* s _ w )+( P 6* P 12* s _ p )+( P 1* P 7* l _ rrm )+( P 2* P 8* l _ w )+( P 3* P 9* l _ p )
 
where s_rrm, s_w, pl, etc. are as set forth above.
 
     As noted above in connection with the exemplary EQUATIONS 4 and 5 for computing, respectively, the promotion and demotion scores, the same set of coefficients may be used. Alternatively, an embodiment may, however, use a different set of coefficients for computing the promotion and demotion scores. 
     In a multi-tiered storage system as described herein, an application having its data stored on thin devices of a storage group may be allowed to use multiple tiers of storage. In order to be able to use the storage of the tiers efficiently and also move a minimal number of chunks between tiers, chunks which are the most active or “hot” need to be located in the higher tiers (e.g., promoted to such tiers if not already located there) and chunks which are least active or “cold” need to be located in lower storage tiers (e.g., demoted to such tiers if not already located there). After identifying the hot and cold chunks, processing may be performed to determine how much of the hot chunks should be placed in the different storage tiers in order to efficiently utilize the higher performing tiers, such as flash tiers, while also avoiding overloading any given tier with I/O request or I/O transfer activity to the point that overall performance (e.g., across all tiers in the AP, across one or more SGs, for the whole data storage system, and the like with respect to the physical devices under consideration) would have been better had less of the workload been placed in the tier. In connection with the foregoing, techniques are described in following paragraphs which determine promotion and demotion thresholds of a data movement policy that may be associated with one or more SGs. The data movement policy as described herein in the context of thin devices affects what data portions of thin devices are data movement candidates and may be moved to another tier. The selection of promotion and demotion thresholds may be made by considering criteria including performance limits (e.g., response time, number of I/Os per time period, and the like) and capacity limits. The performance limits may be flexible or adaptable and specified for each storage tier. The capacity limits may also be specified for each storage tier and may include capacity limits included in an AP for the affected one or more SGs. The techniques model response time of target storage tiers when evaluating different alternative hypothetical considerations in which performance limits are varied for each tier when selecting promotion and demotion thresholds. The different sets of performance limits in combination with capacity limits are evaluated by modeling the expected target tier performance and then determining an overall performance metric representing an aggregate modeled performance metric across all target storage tiers for all affected SGs. In one embodiment, the overall performance metric may be an average response time determined with respect to all target storage tiers using the modeled response time as determined for each such tier. The average response time is used to compare the overall modeled performance for the storage tiers when evaluating different sets of performance limits for each target tier. Each set of performance limits specified for multiple tiers may be used as a way to provide weighting factors for I/O workload distribution across the tiers in order to reflect the performance differences of the different tier storage technologies. Utilizing such “what if” analysis to evaluate different sets of performance limits coupled with capacity limits provides for determining promotion and demotion thresholds that may be used by the DA, or more generally, other backend data storage system components, in connection with performing data movements in accordance with workload or performance impact across all target storage tiers to increase overall performance. 
     In connection with techniques herein as mentioned above, response time may be considered as performance criteria alone, or in combination with other performance criteria in combination with capacity limits, when determining promotion and demotion thresholds affected what data portions of a thin device may be moved between physical storage devices in different storage tiers. The techniques herein consider different performance characteristic information and curves that may vary with each storage tier, type of physical device, device vendor, and the like. In particular, performance curves for the different storage tiers may be determined and used to model target tier and also overall SG performance across storage tiers as part of processing to evaluate different sets of performance limits in combination with capacity limits. As an example, consider a workload of N I/O operations/second. The response time experienced for the same workload varies with storage tier due to the underlying capabilities of each tier&#39;s technology. As such, performance curves may be used in connection with techniques herein to model expected response times if a particular data movement is performed in accordance with candidate promotion and demotion thresholds. 
     Referring to  FIG. 13 , shown is an example of performance characteristic information illustrated in the form of curves for different storage tiers such as may be based on different disk drive types. The example  550  illustrates general curve shapes as may be associated with a SATA drive (as represented by  552 ) and an FC disk drive (as represented by  554 ) in connection with processing rate (X-axis in terms of IOs/second) vs. response time (Y-axis). As may be seen from the illustration  550 , for a same processing rate of I/Os/second, different RTs are obtained for each of a SATA drive and an FC disk drive. As such, when moving data storage tier of SATA drives to a storage tier of FC drives, differences in performance characteristics such as response times are taken into consideration in accordance with techniques herein. An embodiment may store data as represented by the curves of  FIG. 13  in one or more tables having rows and columns of data point values (e.g., X and Y coordinates for a plurality of points). When stored in tabular form, interpolation, curve fitting techniques, and the like, may be used in connection with determining values of X and Y coordinates lying between two existing points stored in the table. When considering moving data between devices of different types or more generally having different device characteristics, such tables of performance characteristic information may be used to determine, for a given processing rate of I/Os per second, a modeled RT for each of the different device types. For example, consider a first storage tier of SATA drives and a second storage tier of FC disk drives. In modeling performance based on a proposed data movement, an aggregated or total processing rate for each target tier may be determined, for example, using performance data collected. For such a total processing rate on the X-axis, a corresponding modeled RT value (Y-axis) may be obtained for each storage tier using tables or curves, such as illustrated in  FIG. 13 . An embodiment may use appropriate performance curves for each of the different storage tiers and associated technologies of the tiers. The performance curves may be obtained for each storage tier based on observed or collected data through experimentation. The particular parameters or metrics of collected data used to obtain performance curves to model expected RT may vary with storage tier and underlying technology. For example, as described in U.S. patent application Ser. No. 12/924,361, filed Sep. 24, 2010, TECHNIQUES FOR MODELING DISK PERFORMANCE, which is incorporated by reference herein, performance curves for modeling response times for disk drives is described using total number of I/Os and I/O size. Other technologies such as flash-based drives may use other parameters in modeling to determine the appropriate performance curve. For example, one approach to modeling flash-based drives may utilize observed performance data related to total number of I/Os, I/O size, and a ratio of read operations/write operations. Additionally, data modeling for different storage drives may utilize a feedback process. At a point in time, there is a set of data representing the performance curve for a particular drive. The actual measured RT of the drive for a given workload in terms of I/Os per second, for example, may be compared to a modeled RT value determined using the performance curve for similar model parameter values. Adjustments may be made to the modeled performance curve based on differences between the measured RT and modeled RT. 
     In connection with estimating thin device workloads, various metrics that may be used are described herein and also in U.S. patent application Ser. No. 12/924,396, filed Sep. 25, 2010, TECHNIQUES FOR STATISTICS COLLECTION IN CONNECTION WITH DATA STORAGE PERFORMANCE, which is incorporated by reference herein. Workload for thin devices may be determined in a variety of different ways in connection with determining the contributions of the thin device data portions that may be stored in multiple thin device pools. One approach may be to examine the allocation map and determine the workload of data portions based on I/Os directed to the physical device where such data portions are stored. However, an embodiment may use alternative approaches to estimate thin device workload due to additional resources consumed in connection with use of the allocation map which may adversely impact performance. When data portions of a thin device are moved from a first storage tier to a second storage tier, the related workload of such data portions are moved to the target tier. In one embodiment, storage for thin devices may be evenly distributed across a pool of data devices comprising a thin device pool. This results in even distribution of capacity and I/O workload thereby making it possible to correlate I/O workload and capacity allocation at the pool level rather than reading the allocation map for each thin device. In other words, a workload for a thin device data portion having storage allocated from a thin device pool of data devices may be estimated by collecting thin device pool statistics and then apportioning an amount of the workload indicated by the collected data distributed evenly across all data portions stored in the pool. 
     In connection with  FIG. 13 , it should be noted that the performance curve of modeled response time is a function of I/O rate (e.g. IOPS or I/Os per second). Performance curves may also be modeled for response time as a function of IOPS and also I/O size for the different storage tiers (e.g., physical device characteristics of physical devices in a particular tier). 
     Referring to  FIG. 13A , shown is an example  600  illustrating a performance curve for modeled response time as a function of IOPS (Y-axis) and I/O size (average for physical drive in kilobytes (KBs)) for a 7.2K RPM rotating drive. Element  602  illustrates a scale of response times from 0-40 milliseconds where the particular pattern indicated on the scale for a response time is denoted on the X-Y graph of  600  for various combinations of IOPs and I/O sizes. Based on the example  600 , it may be generally observed that the I/O size does not have a significant or substantial impact on response time (e.g., response time is not highly dependent on, or sensitive to changes in, I/O size) for the particular physical drive. 
     Referring to  FIG. 13B , shown is an example  620  illustrating a performance curve for modeled response time as a function of IOPS (Y-axis) and I/O size (average for physical drive in kilobytes (KBs)) for a 10K RPM rotating drive. Element  622  illustrates a scale of response times similar to  602  where the particular pattern indicated on the scale for a response time is denoted on the X-Y graph of  620  for various combinations of IOPs and I/O sizes. Based on the example  620 , it may be generally observed that I/O size for the 10K RPM rotating disk drive has a slightly greater dependency than that of  FIG. 13A  but that the I/O size for the 10K RPM does not have a significant or substantial impact on response time (e.g., response time is not highly dependent on, or sensitive to changes in, I/O size) for the particular physical drive. 
     Referring to  FIG. 13C , shown is an example  630  illustrating a performance curve for modeled response time as a function of IOPS (Y-axis) and I/O size (average for physical drive in kilobytes (KBs)) for a 15K RPM rotating drive. Element  632  illustrates a scale of response times similar to  602  where the particular pattern indicated on the scale for a response time is denoted on the X-Y graph of  630  for various combinations of IOPs and I/O sizes. Based on the example  630 , it may be generally observed that I/O size for the 15K RPM rotating disk drive has a slightly greater dependency than that of the 10K RPM drive of  FIG. 13B  but that the I/O size for the 15K RPM does not have a significant or substantial impact on response time (e.g., response time is not highly dependent on, or sensitive to changes in, I/O size) for the particular physical drive. 
     Referring to  FIG. 13D , shown is an example  640  illustrating a performance curve for modeled response time as a function of IOPS (Y-axis) and I/O size (average for physical drive in kilobytes (KBs)) for an exemplary SSD drive such as an EFD. Element  642  illustrates a scale of response times similar to  602  where the particular pattern indicated on the scale for a response time is denoted on the X-Y graph of  640  for various combinations of IOPs and I/O sizes. Based on the example  640 , it may be generally observed that I/O size for the EFD has a significant or substantial impact on response time (e.g., response time is highly dependent on, or sensitive to changes in, I/O size) for the particular physical drive. 
     Referring to  FIG. 13E , shown is an example  650  illustrating a performance curve for modeled response time as a function of IOPS (Y-axis) and I/O size (average for physical drive in kilobytes (KBs)) for another exemplary SSD drive such as an EFD. The example  640  of  FIG. 13D  may represent the modeled performance curve for one type of EFD such as by one vendor based on one vendor&#39;s technology and implementation and the example  650  of  FIG. 13E  may represent modeled performance curve for another type of EFD such as by a different vendor or EFD drive with different performance characteristics than that modeled in  FIG. 13D . Element  652  illustrates a scale of response times similar to  602  where the particular pattern indicated on the scale for a response time is denoted on the X-Y graph of  650  for various combinations of IOPs and I/O sizes. Based on the example  650 , it may be generally observed that I/O size for the EFD has a significant or substantial impact on response time (e.g., response time is highly dependent on, or sensitive to changes in, I/O size) for the particular physical drive. 
     As such, based on the performance curves of  FIGS. 13A-13E , an embodiment may select values for coefficients or weights P 7 -P 12  when determining various promotion and demotion scores in connection with following techniques based on the target storage tier. The target storage tier may be the tier for which processing is performed to select data portions for movement to the target tier. In other words, if processing is determining which data portions may be moved to, or stored on, the flash or SSD tier, values for P 7 -P 12  may be selected in accordance with the expected I/O sizes for each of the I/O operation types as described above. If processing is determining which data portions may be moved to, or stored on, rotating disk drives or, more generally, on a tier including non-flash drives or non-SSD drives, values for P 7 -P 12  may be selected as 1 to reflect the fact that there is not a substantial dependency of I/O size for the particular drive on response time. 
     In connection with estimating modeled response times, performance curves such as that of  FIGS. 13, and 13A-13E  may be used based on the particular parameters considered when modeling the response times. For example, an embodiment may use  FIGS. 13 and 13A-13C  when modeling response times for promotion and demotion scores used with P 7 -P 12  having values of 1 for different types of rotating disk drives. An embodiment may use  FIGS. 13D-13E  when modeling response times for promotion and demotion scores used with P 7 -P 12  having values of determined as a function of I/O sizes. 
     The determination of the optimal tier for each extent of storage is driven by the goal of maximizing the chances of achieving storage performance objectives. This goal will tend to be achieved if the storage system can arrange for the largest share possible of the storage request workload to be serviced by the highest performing storage tiers. In connection with techniques herein, scores, such as the promotion and demotion scores, may be metrics or measurements used to drive this determination process where such scores may be calculated for each storage extent. The input to the calculation is information about the expected storage request workload that the storage extent will receive. The promotion and demotion scores as described herein provide a measure of how ‘well suited’ a storage extent is for placement on a first higher performing storage tier as opposed to a second lower performing storage tier. If the promotion and demotion scores used for the storage tiers are defined properly, then for a first promotion score used when determining what extents to store in an EFD storage tier, if storage extent or portion A has a higher promotion score than storage extent B, then storage extent A is better suited for placement on the EFD tier than storage extent B. Of key importance here is how an embodiment quantifies ‘well suited’. Informally, a storage extent should be considered more ‘well-suited’ for a high tier, such as an EFD tier, if placing the storage extent on the high tier tends to allow a greater number of storage requests (especially RRM requests) to be packed into the high tier. The foregoing provides for use of scores or metrics which guide the selection of the best extents to place on the different tiers. 
     The use of promotion and demotion scores with properly selected weights or coefficients P 1 - 12  allows the storage system to identify the best extents to place in the different storage tiers in a computationally efficient manner. As described herein, for example, processing may be performed to sort the storage extents according to their promotion scores as may be used when determining which extents are best suited for the EFD storage tier. A sufficient number of the extents with the highest promotion scores may be selected to fill the EFD tier based on any one or more of storage capacity limits and/or performance limits for the EFD tier. Similarly, other tiers may be filled with selected extents using scores determined using other values for weights P 1 -P 12  selected for non-EFD tiers. This approach has the important property that it scales well to configurations involving very large numbers of storage extents. 
     As described above, the promotion and demotion scores may have values selected for P 7 -P 12  (e.g., for the size or data transfer weights) to express any existing dependency of the score on I/O size. Weights P 7 -P 12  may have values selected which are always 1 for non-EFD tiers or, more generally, for those storage tiers having drives which do not have a substantial affect on response time or other performance metric used to measure system performance. 
     To illustrate how values for P 7 -P 12  may be selected for an EFD tier where such values for the EFD tier may not always be 1 and may depend on the expected storage tier workload characteristics and the particular performance characteristics of the drives in the high tier, consider the case of a multi-tier storage configuration that includes a storage tier comprised of Enterprise Flash Drives (EFDs). Generally speaking, EFD drives are considered high performing because they are particularly efficient at I/O operation processing (much more so than rotating drives are). However, the data transfer rates supported on EFD drives are not correspondingly large in comparison to data transfer rates for rotating disk drives. As known in the art, data transfer rate or throughput may be characterized as the speed at which data can be transferred between devices. For example, data transfer rates may be expressed in terms of Mbps (amount of data transferred for given unit of time). As such, an embodiment may perform processing to select values for P 7 -P 12  dynamically for the EFD tier by considering the expected workload on the EFD storage tier and the specific performance characteristics of the EFD drives underlying the EFD storage tier. For example, if the expected workload on the EFD storage tier is light enough that the data transfer limits of the EFD drives will not be approached, then an embodiment select size weights (e.g., values of P 7 -P 12 ) for use with EQUATION 4 that are one to thereby allow the greatest number of storage requests to be packed into the available storage capacity in the EFD storage tier (e.g., an embodiment may use a function for EQUATION 4 that assigns a value of 1 to parameters P 7 -P 12 ). If the expected workload on the EFD storage tier is heavy enough to approach the data transfer limits of the EFD drives, then an embodiment may select size weights (e.g., values of P 7 -P 12 ) for use with EQUATION 4 that place greater weight on storage requests with small I/O sizes will allow the greatest number of storage requests to be packed into the available data transfer capacity of the EFD storage tier (e.g., an embodiment may use a function for EQUATION 4 that assigns larger values to parameters P 7 -P 12  for data portions with smaller I/O size). 
     An embodiment in accordance with techniques herein may use models of drive performance to determine whether the performance of the drives in a storage tier is data transfer limited, and the values for P 7 -P 12  may be assigned accordingly as a function of varying I/O size for the different operation types as described elsewhere herein. With scores for the EFD storage tier, there is a preference to have extents in this EFD tier which have higher IOPS of smaller sizes. Therefore, an embodiment may select values for P 7 -P 12  providing a bias or greater weight to a short term or long term metric when the operation type (e.g., RRM, SRM, or W) has smaller expected I/O sizes. 
     To illustrate how an embodiment may select values for P 1 -P 6  (e.g., operation type weights) for use in the scores for a tier where such values for P 1 -P 6  may depend on the expected storage tier workload characteristics and the particular performance characteristics of the high tier drives, let us again consider the case of a multi-tier storage configuration that includes a storage tier comprised of EFDs. Suppose performance of a particular type of EFD drive is particularly sensitive to the amount of write activity on the drive, with performance degrading for larger amounts of write activity. An embodiment in accordance with techniques herein may take this into account when selecting values for P 1 -P 6 . For example, consider a first case where, if the total amount of write workload that may be delivered to the EFD storage tier is light enough to not degrade the performance of the EFD drives, then the scores calculated for the EFD tier may use a small positive (or zero) values for P 2  and P 5  associated, respectively, with the short term and long term W statistics (e.g., s_w and l_w from EQUATIONS 4 and 5), and relatively large values for P 1  and P 4  associated, respectively, with the short term and long term RRM statistics (e.g., s_rrm and l_rrm from EQUATIONS 4 and 5), since this allows the greatest number of RRM requests to be packed into available EFD storage. However, consider a second alternative case wherein, if the total amount of write workload that may be delivered to the EFD storage tier is heavy enough to degrade the performance of the EFD drives, then the scores may select lower (e.g., in comparison to those for the first case) or negative values for P 2  and P 5  associated with the short term and long term W statistics, and yet larger values for P 1  and P 4  (e.g., larger than in the first case) associated, respectively, with the short and long term RRM statistics. This selection in the second case allows the greatest number of RRM requests to be packed into available EFD storage. An embodiment in accordance with techniques herein may use models of drive performance to determine whether the performance of the drives in a storage tier is write performance limited, and values for P 1 -P 6  may be assigned accordingly. 
     More generally in connection with selecting values for P 1 -P 6  of the scores, values may be selected depending on how much preference is given to provide better performance (such as better response time) for a particular operation type (e.g., R vs. W, or preference ordering of multiple types RRM, SR, and W). For example, an embodiment may give higher weight to RRM over W since it is more likely that there is an application waiting for the data of the RRM operation to complete before the application can further proceed with processing. An embodiment may give less weight to sequential read (SR) operations than RRM operations. Write operations and associated statistics in the scores may be given the least relative weight because a waiting host or application performing the write may receive an acknowledge that the operation is complete once the write data is written to cache rather than having to wait for data to be read from a physical drive as with any type of read miss operation (e.g., RRM and SR collectively). 
     As discussed elsewhere herein, policies may be used to determine when to promote data (map the data to a relatively faster tier) and when to demote data (map the data to a relatively slower tier). In particular, one such policy is a data movement policy based on promotion and demotion thresholds that may be determined using promotion and demotion scores for data portions. In an embodiment herein, this may be performed by first determining a score for different portions of a storage space based on relative activity level and then constructing promotion and demotion histograms based on the different scores and the frequency of each. In connection with thin devices, each of the data portions may correspond to a logical extent for which such scores are determined. Exemplary ways in which the promotion and demotion scores may be calculated are described above. The promotion and demotion scores may be used, respectively, in connection with the promotion and demotion histograms described below in more detail. Generally, the scores may be characterized as reflecting the I/O benefit to the host application and cost (e.g., in terms of performance bandwidth) to the targeted storage device tier. In connection with constructing the histogram, all extents are ordered or sorted according to their scores, from highest to lowest. Those extents having the highest scores are generally those preferred to be selected for having storage allocated from the highest performing tier. The histogram is one way in which such scores may be sorted and utilized in connection with techniques herein. It will be appreciated by those of ordinary skill in the art that there are alternative ways to define and compute the scores than as described herein. In one embodiment described herein, the scores may be computed differently for promotion and demotion to reflect the difference in criteria related to data movement into and out of storage tiers. 
     For purposes of illustration, consider an example of a single SG which may use a group of data devices, and thus physical devices, in three thin device pools—one for each of three storage tiers such as illustrated in  FIG. 8A . Workload statistics such as described in connection with  FIG. 10  may be computed for each extent and a promotion score may be calculated for each extent in the SG. Also, assume that only thin devices managed in accordance with techniques herein for which data movement may be performed are located in the SG and use the foregoing thin device pools. In this example, the three storage tiers may include a first storage tier of EFDs, a second storage tier of FC rotating disk drives and a third storage tier of rotating SATA disk drives where storage tiers 1-3 are correspondingly ranked highest to lowest as performance tiers. 
     In connection with techniques herein, assume a first set of promotion scores are determined using a first promotion score having weights or coefficients selected for the first or highest storage tier to be filled, the EFD storage tier. A first promotion histogram described below in connection with  FIG. 14  may be produced using the first set of promotion scores for filling the EFD storage tier. 
     Referring to  FIG. 14 , a histogram  1000  illustrates a plurality of activity bins (buckets) and the frequency thereof. Each vertical line of the histogram  1000  represents a bin corresponding to a number of data portions (e.g., extents) having the corresponding score. Determination of a score for a data portion is discussed in more detail elsewhere herein. In an embodiment herein, there are five thousand bins. Of course, a different number of bins may be used instead. The height of each bin represents a number (frequency) of data portions having a particular score. Thus, the longer a particular vertical line, the more data portions there are having the corresponding score. Note that the sum of all of the frequencies of the histogram equals the total number of data portions of the system. Note also that the sum of frequencies of a portion between a first score and a second score equals the total number of data portions having a score between the first and second scores. As such, the total capacity allocated for a particular bin assuming a fixed size data portion may be determined as the mathematical product of the frequency of data portions in the bin (of those data portions having allocated storage) and the size of a data portion. If the data portions in a bin may have varying size, then such sizes corresponding to the allocated storage amounts for the data portions may be summed to determine the total capacity of storage allocated for the bin. In a similar manner, the modeled response time (e.g., average) for the total cumulative workload (e.g., total I/Os/second) and optionally also based on I/O size of those data portions may be determined. The histogram  1000  also shows a first range indicator  1002  that corresponds to bins having a score from S 1  to SMAX (the maximum score). In the embodiment herein, there are three levels or tiers of physical storage and data portions of the thin device having a score corresponding to the first range indicator  1002  are promoted (mapped) to a highest (fastest) level of storage and data portions having a score corresponding below S 1  are mapped to other storage tiers described below. Thus, S 1  may represent the promotion score corresponding to the promotion threshold for the first or highest storage tier so that all data portions having a score at or above S 1  are promoted to the highest storage tier, or otherwise considered a candidate for such promotion if not already located in the highest storage tier. 
     In a similar manner, for those extents or data portions which do not have first promotion scores at or above S 1 , a second set of new promotion scores may be determined whereby each promotion score of the second set is based on a second promotion score using values for P 1 -P 12  selected for the next fastest or second storage tier to be filled (e.g., the FC rotating disk drive tier in this example). As described herein, values for P 7 -P 12  may all be 1 with values for P 1 -P 6  selected as may be determined dynamically or based on fixed values. Based on these new second promotion scores, a second promotion histogram is constructed as described in connection with  FIG. 14  with the differences that 1) the scores are the second promotion scores for those data portions not previously mapped to the first storage tier using the first promotion histogram, and 2) the threshold S 1  now denotes the promotion score corresponding to the promotion threshold for the second storage tier (next fastest) so that all data portions having a score at or above S 1  are promoted to the second storage tier, or otherwise considered a candidate for such promotion if not already located in the second storage tier. Since this example only includes three storage tiers, those data portions having a second promotion score below the threshold are mapped to (stored in) the lowest or third storage tier of SATA drives. 
     It should be noted that above-noted two promotion histograms of  FIG. 14  used in connection with promotion scores and also in connection with demotion histograms such as in  FIG. 16  (described below) in connection with demotion scores may include scores for all data portions under consideration or analysis. For example, as described elsewhere herein in connection with other examples, the techniques herein may be performed with respect to a number of storage groups of thin devices having their storage allocated from one or more storage pools so that the thin devices have storage allocated from a set of physical drives. In this case, the histograms may include scores with respect to the foregoing data portions of the number of storage groups under consideration and evaluation with the techniques herein. 
     It should be noted that an embodiment using a histogram may select a suitable number of bins or buckets and an interval for each such bin. In one embodiment, the size of each bin may be driven by a selected number of bins with each bin having the same size. Additionally, an embodiment may use different techniques in connection with mapping or converting the promotion and demotion scores to indices associated with histogram bins. For example, an embodiment may use linear scaling to set a lower boundary for buckets having an associated index lower than a selected pivot value and may use logarithmic scaling to set a lower boundary for buckets above the pivot. Logarithmic scaling may be appropriate in embodiments having larger scores or a wide range of scores in order to scale the size of scores above the pivot. In such embodiments, the score range associated with a bucket interval above the pivot varies so that a reasonable number of data portions are mapped to the associated bucket. Whether a histogram or other suitable technique is used may vary with the number of buckets, the number of data portions, and the like. 
     Additionally, it should be noted that rather than have a histogram with frequency on the Y-axis as in  FIG. 14 , an embodiment may represent the total allocated capacity on the Y-axis of the number of data portions having scores within a particular bin. In other words, the height of the bucket or bin represents the total allocated capacity of the scores mapped to that bin. Other representations are possible besides histograms in connection with determining promotion thresholds and also demotion thresholds as described elsewhere herein in more detail. 
     In connection with determining the first tier promotion threshold S 1  of  FIG. 14 , processing is performed to map a number of data portions to the highest performing tier in accordance with criteria including a combination of one or more capacity limits and one or more performance limits. A capacity limit may be specified for each storage tier for the SG in an AP associated with the SG as described above. Additionally, a capacity limit indicating the physical maximum amount of storage capacity as a physical characteristic of the drives may also be considered since it may be possible in some embodiment to exceed the maximum capacity of the drives prior to exceeding the capacity limits in accordance with an AP. Additionally, one or more sets of performance limits may be specified for each storage tier. In one embodiment, performance limits may be specified in terms of response time for each tier. An embodiment may define one or more sets of predetermined response time performance limits for storage tiers where such sets of response time limits may also referred to as performance or comfort zones. Each set contains a response time limit for each storage tier that may be the target of promotion. In one embodiment, limits are not specified for the bottom tier. In one embodiment, seven comfort zones may be specified where each zone includes a response time limit for the first highest performing storage tier, such as flash-based tier, and the second highest performing tier, such as FC disk drives. For example, the following response time performance limits may be specified for seven comfort zones in the embodiment having 3 storage tiers: 
                                     Comfort   EFD/flash   FC disk       Zone   Response Time (ms)   Response Time (ms)                  1   1    6       2   2   10       3   3   14       4   4   18       5   6   25       6   8   40       7   10    50                    
Of course, an embodiment may provide any number of comfort zones more or less than seven and for a different number of storage tiers. Additionally, the foregoing values are exemplary and may vary with technology, drive vendor, and the like. Generally, values specified as the performance limit metrics, such as response times, may vary with the workload and/or other workload characteristics (such as I/O size) of a particular system and may be determined in any suitable manner. For example, values for the foregoing metrics may be made based on knowledge regarding particular workload of a system and typical performance of drives of different storage tiers in a system. In this manner, limits specified may be realistic and in accordance with typical workload performance within a system. It should be noted that the foregoing limits may also be selected based on end user performance requirements. Additionally, as noted elsewhere herein, although response time is used as the workload or performance metric in connection with the foregoing comfort zones, other performance criteria metrics may be used in combination with, or as an alternative to, response time. For example, an embodiment may use utilization as a metric in a manner similar to response time in connection with techniques herein. That is, just as comfort zones include response time limits for storage tiers, comfort zones may include other criteria such as a utilization for each storage tier. As known in the art, utilization of a resource, such as a physical drive or with respect to physical drives of a storage tier, may be defined as a metric measuring an amount of time a device is utilized or in a non-idle state. For example, utilization for a storage tier may be represented as a percentage (e.g., based on a ratio of an amount of time the physical devices of the storage tier are in the non-idle state/total amount of time). The foregoing utilization metric may represent the average utilization for a storage tier determined over a period of time.
 
     Generally, processing may be performed to determine a set of promotion thresholds for the different storage tiers (e.g., S 1  of  FIG. 14 ) in accordance with criteria including capacity limits and a set of performance limits for a single comfort zone. In connection with the above-mentioned first promotion histogram used when mapping data portions to the first or EFD storage tier, processing traverses the first promotion histogram, from highest score to lowest score, mapping data portions to the first storage tier until either the capacity limit for the first storage tier is reached or until the response time performance limit for the first storage tier is reached. 
     Similarly, in connection with the above-mentioned second promotion histogram used when mapping data portions to the second of FC storage tier, processing traverses the second promotion histogram, from highest score to lowest score, mapping data portions to the second storage tier until either the capacity limit for the second storage tier is reached or until the response time performance limit for the second storage tier is reached. 
     For each storage tier, a performance counter is maintained indicating a modeled current I/O processing rate (e.g., total IOPS) and associated modeled response time based on those data portions currently mapped to the storage tier. As described elsewhere herein, performance curves such as illustrated in  FIGS. 13 and 13A-13E  may be used in modeling current performance for each storage tier based on data portions currently mapped to the storage tier when traversing the histogram scores. As each bucket or bin of the histogram has its data portions mapped to the first storage tier, the performance counter (indicating an updated modeled tier RT) is updated to reflect the modeled performance for the first storage tier as also including the additional data portions of the bucket now newly mapped to the first storage tier. For example, as a bucket of data portions is mapped to the first storage tier, the performance or workload information attributed to the newly added data portions in combination with those data portions already mapped to the first storage tier may be input to the appropriate storage tier performance model to determine a modeled aggregate response time. For example, as described above, one disk performance model for SATA and FC disk drives may use as the following as modeling inputs—total number of I/Os (e.g., used to determine the number of I/Os per second or other unit of time) and I/O size (or average I/O size of the total number of I/Os considered)—as collected or observed for the data portions. With these modeling inputs for the aggregated data portions mapped to the first storage tier, the modeling technique may use performance curves to determine an estimated or modeled response time for the physical storage devices in the storage tier based on the aggregate workload of the existing data portions currently mapped to the first storage tier and the additional data portions now also mapped to the first storage tier. In a similar manner, processing may track the current amount of storage of the first tier consumed via the mapping so far. After each bucket of data portions is additionally mapped to the first storage tier to hypothetically represent or model movement of such data portions to the first storage tier, a determination may be made as to whether any of the capacity limits or the response time performance limit for the first tier has been reached or exceeded. If so, the score associated with the current bucket is the promotion threshold. Thus, all data portions in buckets higher than the current bucket (e.g., scores exceeding that of the current bucket) are candidates for promotion to the first storage tier. It should be noted that in connection with the foregoing promotion threshold, the score used as the promotion threshold may be the upper limit of the bucket interval (e.g., score range) for the current bucket at which at least one of the capacity limits or response time performance limits was exceeded during histogram traversal. 
     In connection with response time performance modeling for a storage tier, as described elsewhere herein with thin devices, the additional I/Os associated with the data portions being added (via mapping) to a storage pool of a particular storage tier may be modeled as being evenly distributed across drives of the storage pool. In the simplified example described herein with only a single storage pool, the modeled storage pool response time is also the modeled storage tier response time. In the event of multiple storage pools in a single tier where all such pools are used by the SG, an embodiment may choose to evenly distribute the added I/O operations across all drives of the storage pool. As described elsewhere herein, a simplifying assumption is that there are no other consumers of the storage tier capacities than those thin devices under device management using the techniques herein. In the event that there are other types of devices having associated data stored on the storage tiers, the amount of storage consumed and the workload of such device may be considered when determining whether capacity and performance limits have been reached. It should be noted that the even distribution modeling as described above may reflect that which is actually performed by the storage tiers and devices therein being evaluated in connection with thin device storage allocation. If an embodiment allocates thin device storage in a different manner, then such modeling should reflect that which is performed in the embodiment. 
     In a similar manner, a promotion threshold for the second storage tier is determined by performing processing as described above for the first tier with the difference that the processing is performed for the second storage tier until either the capacity limits or response time performance limit of the first zone are reached for the second storage tier. The foregoing capacity limits and response time performance limits vary with each storage tier. Processing that maps data portions to the second storage tier resumes with the second promotion histogram including new second promotion scores for those unmapped data portions from the previous storage tier processing (e.g., those data portions of the first promotion histogram having first promotion scores below the first storage tier promotion threshold). In this manner, data portions which were not mapped to first tier storage are automatically considered for mapping to storage in the next highest tier. At the end of the second storage tier processing for the current zone, the second storage tier promotion threshold is determined. 
     Referring to  FIG. 15 , shown is a flowchart of steps summarizing processing as described above in connection with determining a single promotion threshold for a single target tier using criteria including capacity limits and comfort zone response time limits for the target tier as specified in a single zone of performance limits. Thus, flowchart  1050  may be executed twice to determine, for the first zone, the two promotion thresholds described above respectively for the first and second storage tiers using the first and second promotion histograms. 
     At step  1052 , initialization processing is performed. Step  1052  includes initializing a variable, AMT, that keeps track of the amount of storage portions to zero. Step  1052  also includes initializing an index variable, I, to the maximum score (highest bin). In an embodiment herein, there are five thousand bins, so I would be set to five thousand at the step  1054 . Of course, other numbers of bins are also possible. Following step  1052  is step  1054  where AMT is incremented by FREQ[I], the amount of data mapped to bin I. Following the step  1054  is step  1056  where an updated modeled tier RT (response time) is determined. At step  1058 , a determination is made as to whether any of the capacity limits and/or response time performance limit for the current tier have been exceeded. Step  1058  may include comparing the updated modeled tier RT to the response time performance limit for the current zone and current target promotion tier. Step  1058  may include comparing the current amount of capacity of the target tier consumed via the modeled mapping represented by AMT to the AP capacity limit. As described elsewhere herein, the total capacity consumed across one or more bins may be determined based on the cumulative frequencies of those bins and the amount of allocated storage of the data portions in the foregoing one or more bins. Step  1058  may include comparing the current amount of capacity of the target tier consumed via the modeled mapping represented by AMT to the SG capacity limit such as may be based on the physical drive capacity limits. If it is determined at the test step  1058  that none of the established limits have been exceeded, then control passes from the test step  1058  to a step  1062  where the index variable, I, is decremented. Following the step  1062 , control passes back to the step  1054  for another iteration. If any one or more of the foregoing limits are exceeded, step  1058  evaluates to yes and control proceeds to step  1064  where a score threshold is assigned the value of I. Data portions having a score of I or higher are promoted to the highest level of storage. Following the step  1064 , processing is complete. 
     The methodology for determining score values used to map data portions (indicating promotion candidates) to one or more intermediate storage levels may be similar to that described above in connection with the flow chart  1050 . In the case of second and third intermediate storage levels in this current embodiment with 3 storage tiers though, processing may be performed with respect to the second promotion histogram. In an embodiment having more than three storage tiers, new promotion scores and an associated new promotion histogram may be computed for a next lower storage tier as may be needed depending on whether a new promotion score is used. 
     If a same set of promotion scores is used for determining promotion for two storage tiers, the same promotion histogram may be used. For example, consider a case where there are 4 storage tiers—EFD and three storage tiers of rotating disk drives. A first set of promotion scores and a first promotion histogram may be used as described above to determine which data portions are mapped to the EFD tier. The first histogram may be based on first promotion scores having values calculated with weights P 1 -P 12  selected for the particular EFD tier. Next, a second set of promotion scores may be calculated using a second promotion score different from that used in determining the first histogram. The second histogram may be based on second promotion scores having values calculated with new weights P 1 -P 12  selected whereby P 7 -P 12  may be 1 and P 1 -P 6  may be a suitably selected. With reference to  FIG. 14A , shown is the second histogram whereby S 2  denotes a promotion threshold score for the second tier and S 3  denotes a promotion threshold score for the third storage tier. In this case, when determining data portions mapped to the third storage tier, the index variable I would be initialized to a score that is one less than the lowest score of the next highest storage level, the second storage tier. For example, if storage portions having a score of 4500 to 5000 are assigned to the second storage level, then the index variable, I, would be initialized to 4499 in connection with determining scores for the third storage level just below the second storage level. 
     Once promotion threshold processing has completed for the current zone, demotion threshold processing is performed as will now be described. 
     Referring to  FIG. 16 , shown is a demotion histogram  1100  similar to the histogram  1000 , discussed above which illustrates a plurality of scores and the frequency thereof. The histogram  1100  may be used to determine which of the data portions (if any) may be demoted (e.g., mapped to relatively slower physical storage). In some embodiments, the histogram  1100  may be identical to the histogram  1000 . In other embodiments, the histogram  1100  may be different than the histogram  1000  because the scores for the histogram  1000  used for promotion may be different than the scores for the histogram  1100  used for demotion. Determination of promotion and demotion scores is discussed in more detail elsewhere herein. 
     In one embodiment including three storage tiers—EFD, FC rotating disk drives and SATA disk drives—as described above, a first demotion histogram  1100  may be determined for the EFD storage tier. In a manner similar to that as described above for a first set of promotion scores for the EFD tier, a first set of demotion scores may be determined for the EFD storage tier using first demotion scores having weights P 1 -P 12  selected for the particular EFD storage tier. In the example  1100  of  FIG. 16 , shown is a first range indicator  1104  denoting that data portions have demotion scores less than S 1  may be demoted (mapped) from the EFD first storage tier to one of the remaining two lower or slower storage tiers of physical storage. 
     Subsequently a second demotion histogram may be determined using those data portions which have demotion scores from the first histogram less than S 1 . In other words, those data portions having demotion scores less than S 1  are demoted from the EFD storage tier but now a determination may be made as to which storage tier such demoted data portions are located—the FC or the SATA storage tiers. For those data portions demoted from the EFD storage tier, second demotion scores may be determined for use with a second demotion histogram. The second demotion histogram may be based on second demotion scores having weights P 1 -P 12  selected for the second storage tier of FC rotating disk drives in this example. Thus, the second histogram is similar to the first histogram with reference to  FIG. 16  with the differences that 1) S 1  represents a second demotion threshold whereby all data portions have a demotion score less than S 1  are demoted to the third or lowest storage tier and those data portions having a demotion score more than S 1  are mapped to the second storage tier and 2) S 1  represents the demotion threshold for the second storage tier. 
     In an embodiment, the demotion threshold for a tier may be determined in any suitable manner. For example, an embodiment may select a demotion threshold with respect to demoting a data portion from a storage tier based on the threshold score determined as the promotion threshold for the storage tier. The demotion threshold may be selected as a score that is the same or lower than the promotion threshold. For example, the demotion threshold may be determined using a constant factor by which the promotion threshold for the same storage tier is multiplied. (e.g. promotion threshold for a tier=1.2*demotion threshold for a storage tier). The foregoing may introduce a stationary zone between the promotion and demotion thresholds for a tier where scores falling this stationary zone are neither promoted or demoted with respect to the storage tier. Introduction of the stationary zone may serve as one mechanism that may be included in an embodiment to limit thrashing with respect to repeatedly promoting and then demoting the same data portions having scores which border the promotion or demotion threshold for a storage tier. The demotion threshold may be selected so that it is always equal to or less than the storage capacity for the SG as may be specified in an associated AP. 
     In an embodiment herein, the processing performed for demoting data portions (extents) may be similar to processing described in connection with  FIG. 15  with the difference that processing may be reversed so that, for example, the portions to be demoted to the lowest level of storage may be determined prior to higher storage tiers by initially beginning with setting I in step  1052  to SMIN and incremented in each iteration. In such an embodiment, storage capacity limits and/or performance limits may be utilized as may be provided in connection with an embodiment. For example, an embodiment may not provide performance limits for the lowest/slowest performing tier but may provide such limits for other tiers. In this case, an embodiment may determine demotion thresholds based on the criteria provided (e.g., if performance limits are not provided for the third storage tier (e.g., slowest) then only capacity limits may be used for the third storage tier. 
     In some embodiments, when a data or storage portion (e.g., an extent) is selected for promotion, only active subportions (e.g., subextents) are promoted while inactive subportions remain at their current storage level. In an embodiment herein, a subportion is considered active if it has been accessed in the previous 4½ days and is considered inactive otherwise. Of course, other appropriate criteria may be used to deem subportions either active or inactive. In some embodiments, when a data portion (e.g., an extent) is selected for demotion, the entire storage portion may be demoted, irrespective of activity level of subportions. In addition, in some embodiments, appropriate mechanism(s) may be provided to reduce the amount of data that is demoted so that more data is maintained on relative faster physical storage devices. Each extent may be evaluated for promotion first as described above and then for demotion if it has not otherwise qualified for promotion. If an extent does not qualify for promotion or demotion, then no data movement is modeled for the extent and subsequently the extent is also not a candidate for data movement with respect to a set of criteria (e.g., capacity limits and performance zone limits) currently being evaluating through modeling using techniques herein. It should be noted that an extent that qualifies for promotion may not then subsequently be a candidate for demotion. Thus, a candidate that qualifies first for promotion may then be removed as a possible demotion candidate. 
     After processing is performed for the first and second storage tiers to determine promotion and demotion thresholds using capacity limits and the first zone&#39;s performance limits, an overall performance metric for the SG using the physical drives of the storage tiers just processed is determined. In one embodiment, this performance metric may be the modeled average response time (RT) for the SG across all storage tiers just processed and may be represented in EQUATION 6 as:
 
Average RT=(1/Total I/Os per second)*ΣALL_TIERS(RT of tier*I/O operations per second for the tier)
 
In EQUATION 6, “Total I/Os per second” is the total number or aggregate of I/Os per second across all physical devices of the SG, “Σ ALL_TIERS” is the mathematical summation of the product represented by “(RT of tier*I/O operations per second for the tier)”. It should be noted that the “RT of tier” may represent the average response time of physical devices in a particular tier. Additionally, EQUATION 6 may generally be determined with respect to all SGs and devices thereof currently being evaluated using the techniques herein. The foregoing Average RT may serve as an overall metric regarding performance of the entire SG across all storage tiers considered to determine whether the modeled performance using the response time limits for the first zone is preferable over other response time limits of another zone. The foregoing EQUATION 6 is a weighted average response time calculation that considers the number of I/Os with a given response time. Alternatively, an embodiment may compute an average RT including separate weightings related to technology type. It should be noted in connection with computing the average RT for the SG using EQUATION 6, the RT for each storage tier of the SG is utilized. This RT for each storage tier may be the last modeled RT computed during the histogram traversal as a result of performing promotion and demotion threshold determination and modeling the performance of such proposed data movement candidate data portions. It should be noted that if other criteria, such as utilization, are used in addition to or as an alternative to RT, then an embodiment may compute an overall or average metric across all storage tiers similar to as described above with EQUATION 6. For example, if zones of performance limits are defined for utilization limits for the storage tiers, then a metric for computing average utilization across all storage tiers of devices being evaluated may be used to represent the overall performance criteria used in selecting a set of performance limits in combination with capacity limits, and also the associated promotion/demotion thresholds.
 
     In a similar manner as just described for the first set of performance limits of the first zone, processing is also performed for the next zone 2 (e.g., using the second set of performance limits). Thus, promotion thresholds and an average RT using EQUATION 6 are produced as a result of processing in accordance with capacity limits in combination with performance limits of each zone. After each zone is processed for candidate promotion and demotion thresholds, a determination may be made as to whether to stop further evaluating remaining zones. Such a determination may be made by comparing a first value for the average RT determined using EQUATION 6 for a current zone with second value for the average RT determined using EQUATION 6 for the previously processed zone. For example, after determining promotion and demotion thresholds using zone 1 performance limits in combination with capacity limits (zone 1 scenario) and then zone 2 performance limits in combination with capacity limits (zone 2 scenario), the average RT associated with the zonel scenario may be compared to the average RT associated with the zone 2 scenario. If the average RT for zone 2 scenario does not indicate a sufficient or threshold level of improvement over the average RT for zone 1, then no further zones may be evaluated. An embodiment may define a threshold value that represents the minimum amount of improvement expected in order to continue evaluating further zone scenarios (e.g., determining promotion and demotion thresholds using capacity limits and performance limits for subsequently defined zones). An embodiment may determine a difference in metric values obtained for the average RT for the two zone scenarios to be compared. An improvement between zone scenarios may be determined if there is decrease in the average RT (e.g., lower average RT means better overall performance). This decrease may be larger than the threshold in order for a sufficient level of improvement to be determined. Alternatively, an embodiment may set the threshold value to zero so that any decrease in average RT between scenarios is considered sufficient improvement to proceed with evaluating further zone performance limits in combination with capacity limits. 
     It should be noted that if one of the capacity limits has been exceeded on a preceding iteration of processing for the prior zone, processing using subsequent zones stops. The processing described herein assumes that the lowest storage tier has sufficient capacity to accommodate storage for any data portions not mapped to the other storage tiers. 
     Referring to  FIG. 17 , shown is a flowchart  1200  of steps that may be performed in an embodiment in evaluating and modeling performance for different performance limits in combination with capacity limits in an embodiment in accordance with techniques herein. The steps of  1200  summarize processing described above. At step  1202 , one or more histograms may be constructed. In step  1204 , current zone is set to 1 in connection with commencing processing for the first zone&#39;s performance limits. At step  1206 , promotion and demotion thresholds are determined in accordance with the capacity limits and performance limits of the current zone. Selection of such thresholds is followed by modeling proposed data movements and determining modeled RTs for all storage tiers for the one or more SGs. At step  1208 , the modeled average RT is determined as an overall performance metric across all storage tiers for the one or more SGs. At step  1210 , a determination is made as to whether the first zone is currently being processed. If so, control proceeds to step  1214 . Otherwise, control proceeds to step  1211  where a determination is made as to whether there has been sufficient improvement with respect to the modeled average RT values for the current zone scenario and the previous zone scenario. If step  1212  evaluates to no, processing stops. If step  1212  evaluates to yes, control proceeds to step  1214  where a determination is made as to whether the capacity limit has been reached. Step  1214  may examine any one or more capacity limits defined such as, for example, capacity limits (e.g., per storage tier, overall SG capacity limits, and the like) as may be defined in an AP, physical limits of drive capacities, and the like. If any one of these capacity limits has been exceeded, step  1214  may evaluate to yes and processing may stop. If step  1214  evaluates to no, control proceeds to step  1216  to increment current zone to the next zone. At step  1218 , a determination is made as to whether this is the last zone. If so, processing stops. Otherwise, control proceeds to step  1206 . 
     It should be noted that  FIG. 17  illustrates only one particular way in which the performance limit criteria and capacity limit criteria may be used in connection with selecting promotion and/or demotion thresholds based on stopping criteria. An embodiment may vary the stopping criteria. For example, an embodiment may perform the foregoing evaluation of all zones of performance limits and capacity limit(s) and determine an average RT value across all storage tier using EQUATION 6, for each such zone, without consideration of the stopping criteria at steps  1212  and/or  1214  and then select the performance zone limits resulting in the best relative average RT across all storage tiers. As another variation, an embodiment may terminate processing and evaluation of subsequent performance zone limits upon finding a first such zone having performance limits that results in a modeled average RT that is above a defined threshold. Thus, an embodiment in accordance with techniques herein may vary the stopping criteria specified in connection with  FIG. 17 . 
     Once processing as described in  FIG. 17  is completed, the promotion and demotion thresholds associated with the zone having performance limits resulting in the minimum average RT may be selected for implementation in connection with actually performing the previously modeled data movements. This is described and summarized now with reference to  FIG. 18 . 
     With reference to  FIG. 18 , at step  1302 , performance zone limits are selected having the minimum associated average response time as modeled. It should be noted that if other performance criteria and associated limits, such as in connection with utilization limits described elsewhere herein, is utilized, step  1302  may include considering other overall performance metrics besides the average response time across all storage tiers. For example, an embodiment may also consider the overall average utilization across all storage tiers. If the embodiment utilizes more than one overall performance metric, then step  1302  may include evaluating the combination of the overall performance metrics. For example, an embodiment may weight each overall performance metric in connection with step  1302  to select a particular performance zone and associated limit criteria. At step  1304 , data movements (e.g., promotion and demotions for the multiple storage tiers) may be performed based on criteria including the promotion and demotion thresholds determined for the selected performance zone limits of step  1302 . In step  1306 , performance zones may be re-evaluated as needed using techniques described herein. Additionally, the response time limits of the performance zones may also be modified as needed to adjust for any workload changes in the system. In other words, as described elsewhere herein, the performance zones defined should set forth reasonable response time limits based on workload of the system being evaluated. The performance zones may set forth response time criteria that varies as the system workload may vary in order to appropriately and automatically adjust response time limits to accommodate for such variations in workload dynamically. It should be noted that the re-evaluation at step  1306  may be performed in response to an occurrence of any suitable event. For example, such re-evaluation may be performed periodically (e.g., upon the occurrence of a predefined time interval), in response to measured or observed system performance reaching a threshold level (e.g., when the measured or monitored response time of the data storage system reaches a defined threshold level), in response to a user&#39;s manual selection, and the like. 
     For purposes of simplification, examples above considered a single SG. An embodiment may evaluate multiple SGs in combination if they share physical devices or defined pools of devices so that there is a dependency in that they utilize the same data storage resources. Additionally, there may be other consumers of the physical devices beside those under management of an optimizer or other component using the techniques herein for data movement. For example, there may be devices which not under management of such a component performing data movement using techniques herein for any one or more reasons. When considering the performance limits of storage tiers, an embodiment may determine a performance baseline associated with such devices representing the workload of such devices in the system since such devices may be viewed as having consumed or utilized a portion of the allowable performance limits. The performance baseline may be defined as disk utilization or a response time value that a physical storage device or drive would have if the drive only hosted data storage for devices that are not under management by a component using the techniques herein. In one embodiment this may include those portions of thin devices which may not be moved between physical devices such as between storage tiers. An embodiment may determine the baseline performance in any suitable manner for unmovable thin devices. For example, an embodiment may determine the data or thick devices included in a thin device pool servicing the thin device and obtain performance data for each such data device in the thin pool. There is an assumption that the embodiment provides for an distribution of workload within pool data devices. Performance data may be obtained for each moveable thin device using the thin device pool where such performance data indicates the thin device workload as distributed over data devices of the thin pool. For each such data device, the workload associated with unmovable thin devices may be determined by subtracting the distributed movable thin device workload associated with the data device from the observed workload of the data device. In other words, for a data device, the workload of the data device attributable to the moveable thin device is subtracted from the total workload of the data device. The result of the foregoing is an estimate of the data device workload attributable to non-moveable thin device portions. 
     In connection with the defined performance or comfort zones described herein, it should be noted that such zones are determined for the particular resource or service that may be consumed or utilized. In a similar manner, zones may be defined and evaluated in connection with other resources or services which are consumed or utilized in the data storage system. For example, zones and performance modeling variations may be modeled in connection with varying the amount of cache where cache limits may be placed on data cached for particular thick or data devices, thin devices, and other entities which consume cache. As another example, zones of performance limits may be specified for varying performance limits related to one or more DAs that service physical data storage devices. In a similar manner as described herein for storage tiers of physical devices, different performance criteria may be specified in terms of performance zones of limits. For example, with respect to DAs, utilization may be used as a performance metric for which comfort zones are defined. 
     In connection with avoiding thrashing, described herein are several techniques that may be utilized such as related to using weighting of long term and short term metrics (e.g.,  FIG. 10 ) and using a stationary zone between demotion and promotion thresholds for a storage tier. An embodiment may use different techniques to avoid large changes in promotion and demotion thresholds selected and utilized in successive time periods. An embodiment may determine a running average with respect to promotion and/or demotion thresholds determined using the techniques herein and use the running average as the actual threshold when implementing data movements. The running average of promotion and/or demotion thresholds may be determined, for example, over a period of time, or using N previous threshold values. An embodiment may also increase the number of performance zones evaluated. 
     It should be noted that the criteria which is evaluated using techniques herein may include capacity limits and performance limits. The processing performed herein provides for adaptive tier overloading protection by allowing the system to automatically select from different sets or zones of performance limits as system workload changes. The particular performance limit criteria of response time specified for each tier in each zone is only an example of a performance limit criteria that may be used in an embodiment. For example, performance limit criteria may use one or more other metrics other than response time, such as I/O processing rate (e.g., number of I/Os/second), #reads/second, #writes/second, service time, queue waiting time or wait time, length and/or number of wait queues, and the like. These one or more other metrics may be used alone or in combination with response time limits. Furthermore an embodiment may associate a different weighting factor with each of the different metrics included in performance limits specified for a zone. The weights used for each of the different metric may vary with performance zone. Furthermore, the actual metrics may also vary with performance zone. For example, it may be that for a first zone, a particular response time limit is being evaluated and other performance limit criteria is also included for evaluation. This additional performance limit criteria (e.g., an additional metric) may not considered in evaluation with other response time limits of other zones. 
     Furthermore, the particular overall metric of average response time used to select between evaluated performance zones may vary in an embodiment from what is described herein. For example, an embodiment may use a different metric other than average response time, or may use the average response time metric, alone or in combination with, other overall performance criteria to evaluate and select between performance zone limits. For example, as described elsewhere herein, an embodiment may also use utilization as the performance metric, alone or in combination with, response time. In such an embodiment, comfort zones of utilization values may be specified and an average utilization may be determined across all storage tiers in a manner similar to calculating and using average response time in EQUATION 6. Utilization may also be modeled in a manner similar to response time as described, for example, in connection with  FIGS. 13 and 13A-13E  (e.g, use modeled utilization curves with I/Os per second on the X-axis and utilization on the Y-axis as may be determined through observed and collected data). 
     Described above are techniques where performance limits and capacity limits are included in the criteria used to determine when limits of a storage tier have been reached. The above-mentioned criteria may include performance limits alone, or performance limits in combination with capacity limits. Furthermore, the above-mentioned criteria used in connection with comfort zones may include capacity limits alone without performance limits. 
     As another simpler variation of the techniques herein with the promotion and demotion scores, an embodiment may rather not evaluate multiple alternatives or comfort zones and may rather consider a single set of limits or criteria including limits that may be based on performance limits as described above (e.g., response time) in combination with capacity limits, performance limits alone, or capacity limits of the different storage tiers alone without performance limits in connection with determining data portions such as extents stored in the various storage tiers. Described below is processing for an embodiment using capacity limits alone. However, other above-noted criteria (e.g., performance limits alone or in combination with capacity limits) may be used in connection with such processing. For example, consider an embodiment including three storage tiers—a first tier of SSD or EFD devices and second and third tiers of rotating disk drives. The second tier may be, for example, FC 15K RPM drives and the third tier may be, for example, SATA drives. The three storage tiers may be ranked from first to third, in terms of performance, from highest to lowest accordingly. When evaluating which data portions may be stored on, or moved to, the first highest performing tier of EFD drives, promotion scores for extents as described above may be determined. Values for P 1 -P 12  may be selected based on the expected storage tier workload characteristics and performance characteristics of the EFD drives as described elsewhere herein. For determining which extents may be mapped to the EFD storage tier, values of P 7 -P 12  may be selected, for example, based on the expected I/O sizes for the particular EFD drives. Values for P 1 -P 6  may also be selected, for example, based on the expected amount of write activity to account for the fact that performance of the EFD devices degrades with larger amounts of write activity. In a similar manner, demotion scores may be determined for the extents as described above. A first promotion histogram may be formed as described above in connection with  FIG. 14  for evaluating which extents may be stored in the EFD storage tier as the target tier. Processing as described in  FIG. 19  may be performed to determine which extents may be stored in the first EFD storage tier based on the scores and capacity limit of the EFD tier. 
     Referring to  FIG. 19 , shown is a flowchart of steps summarizing processing as described above in connection with determining a single promotion threshold for a single target tier using criteria including capacity limits for the target tier. At step  1452 , initialization processing is performed. Step  1452  includes initializing a variable, AMT, that keeps track of the amount of storage portions to zero. Step  1452  also includes initializing an index variable, I, to the maximum score (highest bin). In an embodiment herein, there are five thousand bins, so I would be set to five thousand at the step  1452 . Of course, other numbers of bins are also possible. Following step  1452  is step  1254  where AMT is incremented by FREQ[I], the amount of data mapped to bin I. Following the step  1454  is step  1458  where a determination is made as to whether the capacity limit for the current EFD tier have been exceeded. Step  1458  may include comparing the current amount of capacity of the target tier consumed via the modeled mapping represented by AMT to the AP capacity limit. As described elsewhere herein, the total capacity consumed across one or more bins may be determined based on the cumulative frequencies of those bins and the amount of allocated storage of the data portions in the foregoing one or more bins. Step  1458  may include comparing the current amount of capacity of the target tier consumed via the modeled mapping represented by AMT to the SG capacity limit such as may be based on the physical drive capacity limits. If it is determined at the test step  1458  that the established capacity limit has been exceeded, then control passes from the test step  1458  to a step  1462  where the index variable, I, is decremented. Following the step  1462 , control passes back to the step  1454  for another iteration. If the capacity limit is exceeded, step  1458  evaluates to yes and control proceeds to step  1464  where a score threshold is assigned the value of I. Data portions having a score of I or higher are promoted to the highest level of storage. Following the step  1464 , processing is complete for the first storage tier, the EFD storage tier. 
     Once the foregoing of  FIG. 19  is performed for the EFD or first storage tier using the first promotion histogram whereby a first EFD promotion threshold is determined, new promotion scores may be determined for the remaining extents not placed in the first EFD storage tier in connection with  FIG. 19  processing. In other words,  FIG. 19  processing determines which extents include the highest ranked promotion scores of the first promotion histogram where such promotion scores may have coefficients selected for the particular EFD storage tier. Such highest ranked extents may be included in the EFD storage tier up to the capacity limit of the EFD storage tier such as may be specified for the SG. Subsequently, new second promotion scores are determined for the remaining extents where the new promotion scores may have different values selected for the coefficients P 1 -P 12  for the second storage tier. The second promotion scores may use values of 1 for coefficients P 7 -P 12  and may select other suitable values for P 1 -P 6  as described elsewhere herein for the second storage tier. 
     Based on these second promotion scores, a new second promotion histogram as described in  FIG. 14  may be formed and used in connection with determining which extents may be promoted or mapped to second storage tier. To determine which extents may be stored in the second storage tier based on the capacity limits of the second storage tier, processing steps of  FIG. 19  may be again performed using the second promotion histogram. In this manner, those extents of the second histogram having the highest promotion scores may be stored in the second storage tier up to the capacity limit of the second storage tier. The remaining extents may be placed in the third storage tier. 
     Once promotion processing has completed, demotion threshold processing may be performed in a manner similar to that as described elsewhere herein with the difference that only capacity limits are utilized where applicable. 
     In a manner similar to that as described for criteria including only capacity limits to determine a mapping between data portions and physical storage locations on different storage tiers, such criteria may alternatively include, for example, performance limits in combination with capacity limits, or performance limits alone. 
     What will now be described are techniques that may be used in connection with migrating data from an external or source data storage system to a target data storage system including a data storage optimizer, such as the data storage optimizer  138  of  FIG. 3  performing optimizations as described herein. In this case, the target data storage system and its data storage optimizer may not have any knowledge or information regarding the workload and activity level of the external data on the source external data storage system to be migrated to the target system. Such external data to be migrated may have both “hot” and “cold” portions in terms of activity level. However, the target system may not know which portions have which activity levels. 
     In performing the migration, the user or customer typically wants to move or migrate external data to a target location on the target system in a “safe” target tier of the target system. The target tier may be characterized as “safe” or one with which the customer is comfortable using as the target tier in terms of performance and total capacity of the target tier cost consideration given that actual performance information about the external data being migrated is unavailable for use in determining an appropriate target tier for the external data. As an option, the customer may consider a mid-range performance tier to use as the target tier. For example, as described herein, the target data storage system may include number of storage or performance tiers including, from highest to lowest in terms of performance, an EFD tier of solid state (e.g., flash-based) storage devices, an FC tier of rotating disk drives and a SATA tier of rotating disk drives. As described herein, there may be multiple EFD or highest performance tiers including different storage device technologies, there may be multiple FC or mid-range performance tiers such as FC disk drives of different drive rotations (e.g. 10K FC disk drives and 15K FC disk drives), and there may be multiple lowest-performance tiers including SATA drives, ATA drives, and the like. The lowest end performance tier(s), such as SATA disk drives, may also typically provide the highest amount of total tier storage capacity in the data storage system since this is typically the least costly in terms of cost/unit of storage. The highest end performance tier(s), such as EFD, may also typically provide the least amount of total tier storage capacity since storage of this tier typically has the highest cost/unit of storage. The mid-range tier, such as the FC tier(s), typically has a total tier storage capacity between that of the lowest and highest performance tiers and also a cost/unit of storage between that of the lowest and highest performance tiers. 
     In considering whether to use the EFD tier as the migration target tier, the EFD may be too costly and there may also be an insufficient amount of storage capacity for storing the migrated data. In considering whether to use the FC tier as the migration target tier, this may be a preferred balance in terms of cost and capacity but there may also be an insufficient amount of storage capacity for storing the migrated data. Typically, the EFD and FC tiers both have a large amount of their capacity used (or consumed) for currently stored data. As a result, the SATA tier may be the only available tier having such sufficient storage capacity for storing the migrated data. If the SATA tier is selected for use as the target migration tier, the currently unknown workload or activity level of the migrated data may overload the SATA tier. In other words, a problem may occur if the workload or activity of the migrated data placed in the SATA tier is high. Once the data has been migrated to the SATA tier and I/Os are directed to the migrated data, it will take time for the automated techniques described herein to gather performance data on the migrated data and move such data to a higher performance tier more suitable for the higher workload or activity level. In this case, performance of the data storage system may be adversely impacted. For example, performance of the application performing I/O (e.g., reads and/or writes) using the migrated data of the SATA tier may experience unacceptable performance, such as slow response times. Additionally, the number of pending write operations using the migrated data stored in the SATA tier may greatly increase thereby consuming an unacceptable amount of cache used to store write data which is waiting to be written to the physical storage device. The amount of cache consumed may be at the system limits. In connection with the latter, performance of the entire data storage system may be adversely impacted since the cache is a shared resource and used for pending writes of other tiers for pending write data. Furthermore, the migration process may take an unacceptable amount of time to complete due to the adverse impact in performance and available resources of the data storage system. 
     Thus, the preferred storage tier for storing the externally migrated data may be the FC or another mid-range performance tier. However, a challenge for using any such tier as the migration target may be due to the likelihood that the FC tier is highly used for currently stored data under management of the automated storage techniques and data movement optimization whereby there may be an insufficient amount of storage capacity available for storing the data to be migrated from the external data storage system. 
     As an option, the SG policies specifying the capacity limits used by the data storage optimizer may be modified. As described elsewhere herein, the SG and other policies (e.g., such as using the AP or allocation policy described herein) may specify amounts of the various storage tiers which the data storage optimizer may consumer or use in connection with performing data storage movement optimizations. Such an amount specifying a capacity limit for the FC tier may be manually lowered by the customer or user so that the data storage optimizer consumes a lesser amount of the FC tier thereby leaving a remainder of available capacity that is sufficient to store the migrated data. However, this may be undesirable and/or impractical due to the complexity of performing such an operation and the level of knowledge necessary for the user making such modifications. Additionally, after the migration is completed, the user will need to restore the previous capacity limit for the FC tier as prior to the migration. Performing the foregoing may be cumbersome and error prone. 
     As another option without modifying capacity limits of the policies used by the data storage optimizer as described above, processing may be performed to first allocate storage for the migrated data from the FC tier. If this amount is insufficient, then additional storage may be allocated from available capacity of another tier, such as the SATA and/or FC tier. However, drawbacks of allocating storage from the SATA tier are noted above. Additionally, using EFD storage for storing migrated data has a high cost and prevents the data storage optimizer from using such allocated EFD storage for use with other data under its management whereby such other data that may have an activity level more suitable for the EFD tier than the migrated data&#39;s activity level. 
     What will now be described are techniques that may be used in connection with migrating data from an external data storage system to a target data storage system having a data optimizer executing thereon as described elsewhere herein. The target data storage system may have multiple performance storage tiers and automated data movement optimizations performed across such tiers. The techniques described in following paragraphs may be used in connection with data to be migrated to the target data storage system where a current activity level or workload of such data being migrated is unknown. For example, such data being migrated may not have associated performance data denoting the data&#39;s activity and workload. 
     An example is described in following paragraphs to illustrate use of processing that may be performed in an embodiment in accordance with techniques herein. In this example, data is being migrated from an external data storage system (or source data storage system) to a target data storage system. The target data storage system in this example may include three storage tiers—the SATA, FC and EFD storage tiers—as described above. However, the target data storage system may generally include any number of storage tiers for use with techniques herein. 
     Referring to  FIG. 20 , shown is an example  1500  of data storage systems and components that may be used in an embodiment in accordance with techniques herein for performing data migration. The example  1500  includes an external data storage system  1502  including a migration process  1504  and migration source device  1506 . The example  1500  also includes a target data storage system  1512  including data storage optimizer  1514  and one or more devices of three different storage tiers—the SATA tier  1516   a , the FC tier  1516   b  and the EFD tier  1516   c . In the example  1500 , data on device  1506  is to be migrated to the target data storage system  1512 . The migration process  1504  may control the migration process to migrate data of  1506  to the target system  1512 . At this point in time, there may be no performance data available to the target system  1512  characterizing the activity level or workload of the data on  1506 . As a result, the target system  1512  cannot select a particular target tier or pool within  1512  to use as the target of the migration whereby such selection is based on an excepted activity or workload level of the data in  1506 . 
     As a first step, the migration process  1504  may identify an amount of capacity needed on the target system  1512  to store the migrated data. The migration process  1504  may send a message  1510  to the data storage optimizer  1514  requesting the identified amount of capacity needed for the migrated data (e.g., amount of data to be migrated from the source device  1506 ). For example, the migration process may send a message to the data storage optimizer  1514  indicating that 200 GB of data is to be migrated. Additionally, a target storage tier of the target data storage system  1512  is selected. The selected target storage tier is the tier from which storage is allocated for storing the migrated data of  1506 . For example, one of the three storage tiers (e.g., EFD, FC and SATA) is selected as the migration target tier in  1512 . The selected tier may be a default mid-performance tier, as noted above. Alternatively, the tier may be selected using any suitable means. For example, the tier may be selected via user input, read from a configuration file, and the like. The selected tier may be communicated from the migration process  1504  to the data storage optimizer  1514  as also denoted by  1510 . Alternatively, depending on how the migration target tier or pool is selected, rather than have the migration process communicate a selected target storage tier or pool to be used for migration, the target data storage system may provide for selection of the target tier or pool to be used for the migration. 
     It should also be noted that an embodiment may have a target data storage system  1512  configured with multiple storage pools for each of the tiers  1516   a ,  1516   b  and  1516   c . In this case, rather than identify a particular storage tier, one or more storage pools including storage devices of a particular tier may be selected. However, for simplicity in illustration in this example, selection of a storage tier may be made and the selected tier may be the FC tier. 
     As a second step, the data storage optimizer  1514  determines whether the selected pool or storage tier is under management by the data storage optimizer  1514  for use in data storage optimizations such as data movement optimizations. If the requested pool or storage tier is not under management by the data storage optimizer  1514 , the optimizer  1514  then determines whether there is sufficient available capacity in the requested pool or storage tier. For this example, this determination may be made by determining whether the requested pool or storage tier includes at least an amount of available or free storage capacity equal to the requested 200 GB. If there is sufficient available storage capacity to accommodate the migration request, a message  1520  may be returned to the migration process  1502  indicating that there is sufficient available storage of the requested pool or tier in the target system  1512  and the migration process is allowed to commence with the migration. If there is insufficient available storage for the migration in the requested pool or tier which is not under management by the data storage optimizer, a message  1520  may be returned to the migration process  1502  indicating this and failing the request for the current migration storage request. 
     If the requested pool or storage tier is under management by the data storage optimizer  1514 , the optimizer  1514  performs a third step and temporarily reduces its capacity allocation limit to accommodate the amount allocated to the migration process for the migration to be performed. In this example, assume that the optimizer  1514  currently has a 1 TB (terabyte) allocation capacity limit for the FC tier whereby the foregoing 1 TB capacity limit is based on defined policies indicating a maximum storage capacity that can be used by the data storage optimizer in connection with performing its data storage optimizations (such as the data movement optimizations described elsewhere herein). In response to receiving the message from the migration process  1504  indicating that 200 GBs of storage is needed on the target system  1512  for the migrated data, the optimizer  1514  temporarily reduces its capacity limit to 800 GBs. Thus, the optimizer  1514  may be characterized as temporarily reserving an amount of storage from the requested tier or pool for use with the migration process. 
     After the optimizer reduces its capacity limit for the requested tier for the migration, a fourth step is performed whereby the optimizer  1514  determines whether there is sufficient available capacity in the requested pool or storage tier. If there is sufficient available capacity in the requested pool or storage tier, the optimizer  1514  may then return a message  1520  to the migration process  1502  in a manner as described above indicating that there is sufficient available storage of the requested pool or tier in the target system  1512  and the migration process is allowed to commence with the migration. 
     If there is currently insufficient available capacity in the requested pool or storage tier to allocate all the requested storage for the migration, as part of reserving the requested amount of storage of the FC tier for the migration process, the data storage optimizer  1514  performs processing to move or relocate data from the target pool or tier requested by the migration process. In one aspect, the data storage optimizer is currently in violation of its newly revised capacity limit of 800 GB for the FC tier. Thus, the amount of data moved from the FC tier to one or more of the other tiers is an amount sufficient to remove the foregoing capacity violation (e.g., so that amount of the FC tier used to store data portions under management by the data storage optimizer does not exceed 800 GB). 
     The data storage optimizer may select the particular data portions to be relocated from the FC tier to one or more other tiers based on the activity level or workload of such data portions. A first portion of extents in the FC tier having the highest activity level or workload may be relocated to a target tier having a higher performance than the FC tier. For example, the first portion of extents in the FC tier having the highest activity level (of all extents, or more generally, data portions, currently stored in the FC tier) may be relocated to the EFD tier. Additionally, a second portion of extents in the FC tier having the lowest activity level or workload may be relocated to a target tier having a lower performance than the FC tier. For example, the second portion of extents in the FC tier having the lowest activity level may be relocated to the SATA tier. The number of extents in the foregoing first and second portions may depend on one or more factors. An embodiment may decide to only move the first portion of extents from the FC to the SATA tier without also moving data from the FC to the EFD tier. Alternatively, an embodiment may decide to only move the second portion of extents from the FC to the EFD tier without also moving data from the FC to the SATA tier. 
     An embodiment may use any suitable technique to determine the particular amount of data relocated from the migration&#39;s target tier (e.g., the FC tier) to the lower and/or higher performing tiers. For example, an embodiment may have policies with capacity and/or performance limits defined for each tier as described herein. In this case, any data movement may be subject to such limits. For example, data may be moved from the FC to the SATA tier to remove the current violation of the 800 GB capacity limit. However, such movement of data to the SATA tier may also be subject to any capacity and/or performance limits specified for the SATA tier. As a first option, an embodiment may only perform data movements of the lowest activity data from the FC to a lower performing tier such as SATA provided there is sufficient SATA storage and no SATA capacity or performance limit violation. If there is an insufficient amount of SATA storage or a SATA capacity or performance limit violation, any remaining amount of data movement needed may be performed by moving data having the highest activity level from the FC to the EFD tier. As a second option, an embodiment may specify a first default amount or proportion of data to move to the SATA tier and a second default amount or proportion of data to move from the FC to the EFD tier. For example, an embodiment may specify that 50% of the data movements needed are to be from the FC to the SATA tier and the remaining 50% of the data movements are to be from the FC to the EFD tier. 
     Such data movements may be modeled prior to being performed to obtain an estimate of performance (e.g., such as may be measured in one or more metrics such as predicted response time) across multiple storage tiers or the entire data storage system. Any data movements may also be subject to such aggregated performance criteria alone, or in addition to, any other limits specified for capacity and/or performance such as for each storage tier. 
     Thus, an embodiment may select different candidate data portions to be moved from the FC tier to one or more other tiers and may model aspects of such movements prior to being performed to ensure that the selected movements do not violate any policy limits, such as capacity and/or performance limits of each tier, and any other criteria that may be specified. Once the data portions have selected, the data movement may actually be performed and the system waits until such data movements have been completed to make available the required amount of storage to perform the data migration. 
     Once any necessary data movements from the FC to one or more other tiers of the target system have completed, the target data storage system sends a message denoted above as  1520  to the migration process to commence migration of the data. 
     An embodiment may use any suitable technique to detect and determine whether the migration process has failed. For example, during the migration process, a timeout value may be specified denoting an amount of time within which the migration process should complete. If the migration process has not completed by such time, the external data storage system  1502  and/or the target data storage system  1512  may determine that there is a problem with the migration and may take a responsive action such as to abort the migration process. As part of responsive action taken in response to aborting the migration, the target system may perform processing to restore the capacity limit of the FC tier back to its limit of 1 TB as prior to the migration. 
     An embodiment may use a heartbeat technique to detect whether the migration process is “alive”/active or “dead”/has failed such as by having the target data storage system  1512  periodically sending a heartbeat message  1525   a  to the migration process  1504 . The migration process  1504  may respond with a heartbeat response message  1525   b . The foregoing occasional polling by the data storage system may ensure that the migration process has not failed. If no heartbeat response message  1525   b  is received, then the target system  1512  may determine that there has been a problem or failure of the migration process  1504 , external system  1502  and/or connections used for the data migration communications and take a responsive action. For example, the target system  1512  may assume the migration process has failed or been aborted and may take steps to return the target system to the state prior to the migration commencement (e.g., restore the capacity limit of the FC tier back to its limit of 1 TB as prior to the migration, release or unallocated any resources currently allocated for the migration, and the like). 
     Once the data migration has completed, the migration process  1504  sends a completion message  1530  to the target data storage system  1512 . At this point, the target system and the data optimizer  1504  may take steps to return the target system to the state prior to the migration commencement (e.g., restore the capacity limit of the FC tier back to its limit of 1 TB as prior to the migration, release or unallocated any resources currently allocated for the migration, and the like). 
     In selecting the particular extents, or more generally, data portions, to be moved from the FC to another tier, the activity level of each such data portion may be examined in comparison to other data portions located in the FC tier. In this manner, extents may be selected which have the highest activity level of all extents currently in the FC tier for movement to the EFD tier. Also, extents may be selected which have the lowest activity level of all extents currently in the FC tier for movement to the SATA tier. An embodiment may use any suitable performance data characterizing the activity or workload of such data portions. Examples of such performance data are described herein. 
     To further illustrate one way in which data portions of the FC tier may be selected for data movement to increase the amount of available storage capacity in the FC tier for storing the migration data (as the migration target) will now be described. Reference is made to  FIGS. 21 and 22 . 
     Referring to  FIG. 21 , shown is an example  1600  illustrating amounts for capacity limits and currently used or consumed storage capacity in the target data storage system. The illustration  1600  is a furtherance to that as described above where the target system includes a SATA tier, an FC tier selected as the migration target tier and an EFD tier. The data optimizer has capacity limits or maximum amounts of usage as follows: the SATA tier has a 3 TB capacity limit  1602 , the FC tier has a 1 TB capacity limit, and the EFD tier has a 500 GB capacity limit. Additionally, at the point in time at which the data storage optimizer  1514  receives the request  1510  from the migration process identifying an amount of storage needed in the target system to store the migrated data, the data storage optimizer is currently using an amount of storage in the SATA tier as denoted by C 1   1608 , an amount of storage in the FC tier as denote by  1604  and an amount of storage in the EFD tier as denoted by  1612  C 3 . Thus, the amount of storage capacity which is free or available within the target FC tier is denoted graphically by the difference or space between  1604  and  1610 . In this example, the foregoing amount of free storage within the FC tier may be insufficient for the migration target (e.g., less than amount of storage capacity requested by the migration process as described in connection with  1510  of  FIG. 20 ). In this case, processing is performed to move or relocate data out of the FC migration target tier to one or more others of the SATA and/or EFD storage tiers of the target system. 
     With reference now to  FIG. 22 , processing is described with selection of extents within the FC tier for data movement to the lowest performing SATA tier whereby such extents have the lowest activity level of all data portions currently stored in the FC tier. The example  1550  may be histogram of activity data for each of the data portions in the data storage system across all storage tiers. The histogram of  1550  is similar to that as described above, for example, in connection with  FIGS. 14 and 14A  elsewhere herein. The histogram includes on the X axis the range of scores from 0 to SMAX where the Y axis records the frequency or count of each such score (or range of scores) as represented on the X-axis. As described elsewhere herein, scores as represented by the X-axis may be included in buckets or score ranges where the frequency may represent a number of data portions having a score within each of the particular score ranges. The scores may be calculated as described above and represent the activity level of the extents. In this example, the scores for the data portions may be determined using long term and short term performance metrics collected for the data portions. 
     Line  1554  may represent the first score threshold S 1  whereby all scores at or above S 1  may correspond to scores of those data portions having a sufficiently high activity level to be stored currently on the EFD tier. Line  1552  may represent a second score threshold S 2  whereby all scores which are both at or above S 2  and also less than S 1  may correspond to those data portions currently stored on the FC tier. Those data portions having a score which is less than that denoted by S 2  (e.g., to the left of  1552 ) may be currently stored on the SATA tier. In connection with selecting data portions for movement from the FC tier to the SATA tier, an embodiment may consider those data portions having scores which fall in the range  1051  (e.g., scores at or above S 2   1552  and less than S 1   1554 ). Data portions within the FC tier may be ranked in terms of increasing score and thus increasing activity level (from lowest activity level to highest activity level) by examining scores within the foregoing range  1551  between  1552  and  1554  from left to right. In this manner, data portions may be selected for movement in an ordering corresponding to such examination of their corresponding scores representing increasing activity level beginning with the lowest such activity level associated with any data portion in the FC target tier. With reference to  FIG. 22 , scores and associated data portions within  1551  may be considered in a left to right ordering for data movement to the SATA tier until there is a sufficient amount of free or available storage within the FC tier for the migration. In this example, a number of data portion having corresponding scores represented by those between  1552  and  1553  may be selected for movement to the SATA tier whereby, after such data portions or moved from the FC tier to the SATA tier, the FC tier will have a sufficient amount of available storage capacity to accommodate the migration process requested amount. 
     In a manner similar to that as described above with respect to those data portions having the lowest activity level within the FC tier, data portions within the FC tier having the highest such activity levels of all portions in the FC tier for data movement to the higher performing EFD tier. 
     Data portions may be selected for movement from the FC to the EFD tier in an ordering corresponding to such examination of their corresponding scores representing decreasing activity level beginning with the highest such activity level associated with any data portion in the FC target tier. With reference to  FIG. 22 , scores and associated data portions within  1551  may be considered in a right to left ordering for data movement to the EFD tier (e.g., consider scores and associated data portions beginning with the rightmost score below S 1   1554 ). In this example, a number of data portion having corresponding scores represented by those between  1554  and  1555  may be selected for movement to the EFD tier. 
     As described herein, an embodiment may select the total number of required data portions for data movement out of the FC tier in any suitable manner. One embodiment may, for example, first select data portions for movement from the FC to the SATA tier having scores included in the subrange between  1552  and  1553  up to a particular maximum score corresponding to that as represented by  1553 . If additional storage in the FC tier is still needed, an embodiment may then proceed with selecting additional data portions for movement from the FC to the EFD tier whereby such data portions have scores included in the subrange between  1554  and  1555  down to a particular minimum score corresponding to that as represented by  1555 . Thus,  1555  may represent the minimum or lowest possible score of a data portion considered for data movement from the FC to the EFD tier. In any case, data portions may be selected for movement from the FC tier to another tier using the foregoing ordering as determined by one or more rules that may be included in a policy until a sufficient amount of storage is available in the FC target tier to accommodate the migration. It should be noted that in some cases, there may not be a sufficient amount of storage in the collective tiers under management by the data storage optimizer to accommodate both the migration and data portions currently managed by the optimizer. In this case, the request for storage by the migration may be denied thereby failing the migration process. Such denial or failure may be denoted by a message returned from the target system  1512 . 
     An embodiment may partition the storage tiers into a plurality of performance categories where each of the performance categories may include one or more of the storage tiers. With reference now to  FIG. 23 , shown is an example  1800  in which an embodiment having more than the foregoing three tiers may define or group multiple such tiers in each of the following three categories—lowest performance tiers  1802  (category A), mid-range performance tiers  1804  (category B), and highest performance tiers  1806  (category C). The lowest performance tiers  1802  may include a first tier of SATA rotating disk drives and a second tier of ATA rotating disk drives. The mid-range performance tiers  1804  may include a third tier of FC 10K rotating disk drives and a fourth tier of FC 15K rotating disk drives. The highest performance tiers  1806  may include fifth and sixth tiers each of different types of solid state or flash-based drives, such as a tier of SLC drives and a tier of MLC drives. In connection with an example for such an embodiment, the foregoing third tier of FC 10K drives included in category  1804  may be selected as the target tier and data movement may be needed to provide sufficient available storage in the third tier for the migration to commence. In this case, a policy may be defined specifying an order in which data movements are performed to relocate data from the third tier to the various other tiers to increase the amount of available storage of the third tier for the migration. The policy may specify an order in which data movements are performed to particular tiers to make available sufficient storage in the target tier, the third tier, for the migration. The policy may specify to first move a sufficient amount of data from the third tier to any other mid-range performance tier also included in  1804  having available capacity. Thus, data portions may be selected for movement from the third tier of FC 10K drives to the fourth tier of FC 15K drives also included in  1804 . Since the data portions are being moved between tiers in the same mid-range performance tier classification  1804 , an embodiment may have a policy that allows any data portion to be moved between tiers of the mid-range performance classification regardless of (independent of) the activity level of the data portion. Alternatively, since this data movement is from the third to the fourth tier (having a higher performance than the third tier) data portions of the third tier ranked as having the highest activity within the third tier of FC 10K drives may be moved from the third tier to the higher performing fourth tier of FC 15K drives. If additional data needs to be moved from the third tier due to insufficient available capacity in the fourth tier, the policy may specify to move the lowest activity data portions of the third tier to any one or more of the lowest performing tiers of  1802  having available capacity. 
     Referring to  FIGS. 24 and 25 , shown are flowcharts  1900  and  1950  of processing steps that may be performed in an embodiment in accordance with techniques herein. The flowcharts summarize processing described above. At step  1902 , the migration process sends a message to the target data storage system. The message may be received by the data storage optimizer. The message may be a request for an amount of storage needed for the migration whereby such storage is being requested in a particular target pool and/or storage tier of the target system. At step  1904 , the target system receives the message. In step  1906 , a determination is made as to whether the target tier and/or pool to be used for the migration target (e.g., storing the migrated data) is managed by the data storage optimizer. If step  1906  evaluates to no, control proceeds to step  1908  to determine whether there is a sufficient amount of available or free storage in the requested tier and/or pool. If step  1908 , evaluates to no, an error message may be returned in step  1910  to the migration process. If step  1908  evaluates to yes, a message may be returned to the migration process to proceed with the migration in step  1912 . In step  1913 , the migration proceeds and the target system is notified at migration completion. It should be noted that other processing than as described herein may be performed in connection with management and reservation of storage for the migration not under management by the data storage optimizer. For example, such processing may include steps to ensure that the requested storage for migration is reserved for the migration process and not used for another purpose while the migration is in progress. 
     If step  1906  evaluates to yes, control proceeds to step  1914  where the capacity usage limit of the target tier and/or pool is lowered by an the requested amount of storage by the migration process. In step  1916 , a determination is made as to whether the target pool and/or tier currently has a sufficient amount of available storage for the migration? If step  1916  evaluates to yes, control proceeds to step  1954  where a message is sent to the migration process to commence the migration process. If step  1916  evaluates to no, control proceeds to step  1920  to select data portions currently in the target pool and/or tier for movement to one or more other storage tiers of the target system. In step  1952 , the data movement for the portions selected in step  1920  is performed. In step  1954 , a message is sent from the target system to the migration process to commence migration processing. In step  1956 , the migration is performed and completes. In step  1958 , the migration process sends a message to the data storage optimizer of the target system that the migration has completed. In step  1960 , the data storage optimizer&#39;s capacity usage limit for the target tier and/or pool is increased and returned to its state or value as prior to the migration (e.g., returned to its value as prior to step  1914  such as by increasing the limit by the amount of storage temporarily reserved for the migration process). 
     The foregoing may be performed in an automated manner without requiring using interaction and input. 
     In connection with communications between such data storage systems, standardized interfaces may be used to facilitate communication between data storage systems. Such standardized interfaces may be used to facilitate communications in connection with techniques herein. 
     It should be noted that although the techniques described herein are used with thin devices providing virtual storage provisioning, the techniques herein may also be used in connection with other types of devices such as those not providing virtual provisioning. 
     Described above are different techniques that may be performed by an optimizer, for example, such as the optimizer  138  with reference back to  FIG. 3 . As described herein the optimizer may perform processing of the techniques herein to determine how to allocate or partition physical storage in a multi-tiered environment for use by multiple applications. The optimizer may perform processing such as, for example, to determine what particular portions of LVs to store on physical devices of a particular tier, evaluate when to migrate or move data between physical drives of different tiers, and the like. In connection with above-mentioned descriptions, embodiments are described whereby the optimizer may be included as a component of the data storage system, such as a data storage array. In such embodiments, the optimizer may perform optimizations, such as the data movement optimization, with respect to physical devices of a single data storage system such as a single data storage array. Such data movement optimizations may be performed with respect to different data storage units of granularity that may be vary with embodiment and/or type of logical devices. For example, an embodiment may provide for partitioning data of a logical device (as may be stored on one or more physical devices (PDs)) into multiple data portions of any suitable size. The data movement optimization processing may provide for evaluation and data movement of individual data portions (each of which can be much less than the size of entire LV) between storage tiers based on the workload or activity of I/Os directed to each such data portion. As the workload may change dynamically over time, the data storage optimizer may continuously evaluate and perform data movement optimizations as needed responsive to such changing workloads. 
     It should be noted that the target data storage system may be connected to one or more other external data storage systems whereby one or more storage tiers managed by the data storage optimizer of the target data storage system include storage located on such external data storage systems. For example, the target system may include the three storage tiers as described above and also include a fourth storage tier of physical storage devices located on an external data storage system whereby the data storage optimizer of the target system performs automated data movement optimizations between storage tiers including those three tiers having physical devices located on the target system and additionally including physical devices of the tier located on the external data storage system. The external data storage system and its storage may be accessible to a host indirectly through the target data storage system. In this manner, the host or other client may send I/Os to the target system and physical storage for the I/Os may be located on physical device of the target system or another external data storage system connected to the target system. 
     The data storage optimizer may be located in a first or primary data storage system and may perform data storage optimizations, such as data movement and other optimizations, for PDS stored on the first data storage system. Additionally, the optimizer, or more generally, the one or more components performing the optimization processing, may perform data storage optimizations with respect to such externally located data storage systems and PDs. For example, the first data storage system may be connected, directly or through a network or other connection, to a one or more external data storage systems. The optimizer of the first data storage system may perform data storage optimizations such as data movement optimizations with respect to PDs of the first data storage system and also other PDs of the one or more other external data storage systems. In this manner, the data storage optimizer may perform data storage optimizations of its own local devices and/or other devices physically located in another component other than the data storage system. In other words, the techniques herein for performing data movement evaluation, performing the actual movement of data such as between physical devices of different storage tiers, and the like, may be performed by code executing on a component that is external with respect to the data storage system including the physical devices for which such data movement optimization is performed. In such data storage system environments including multiple physical data storage systems, physical storage for the migration target may be located in the first data storage system including the optimizer and/or another external data storage system connected to the first data storage system (e.g., including storage under management or use by the data storage optimizer). For example, with reference to the above-mentioned fourth storage tier including physical devices of an external storage system, such a fourth storage tier may be specified as a target of the migration. 
     For example, with reference now to  FIG. 26 , shown is an example  2800  of a system and network including a host  2802 , data storage system  1  (DS 1 )  2804  and data storage system  2  (DS 2 )  2806 . The data storage optimizer  2801  as may be included in DS 1   2804  may perform data storage optimizations across multiple storage tiers of PDs included in DS 1   2804  and also PDs of DS 2   2806 . The optimizer  2801  may perform optimization processing such as in connection with data movement evaluation for moving data portions of LUNs between different underlying PDs providing the physical storage for the LUNs. DS 1   2806  may provide for presenting to the host  2802  storage on both DS 1  and DS 2 . LUNs A, B and C may be presented as devices of DS 1  where LUN A may have underlying storage provisioned on PDs of DS 1  and LUNs B and C may have underlying storage provisioned on PDs of DS 2 . For example, as illustrated, DS 1  may map LUNs B and C (presented to the host as devices of DS 1 ) to LUNs R 1  and R 2 , respectively, of DS  2 . 
     DS 1  may utilize one or more components providing a “virtualized backend” to DS 2  such as, for example, where a DA of DS 1  communicates with an FA of DS 2  to access LUNs R 1  and R 2  of DS 2 . In connection with SCSI terminology, a port of a DA of DS 1  may be an initiator and a port of an FA of DS 2  may be a target forming a path over which DS 1  may access a LUN of DS 2  (e.g., access one of the LUNs R 1 , R 2 ). Thus, the example  1000  is an illustration whereby the host  1002  communicates directly with DS 1   1004  issuing commands and operations to LUNs A-C. Host  2802  is provided access to storage and devices of DS 2  only indirectly through DS 1 . As such, DS 1  may want to know different types of information about DS 2   2806  (e.g., such as regarding the underlying PD storage from which LUNs R 1  and R 2  of DS 2  are provisioned in connection with providing data services, and other information as described elsewhere herein) in connection with performing data storage optimizations. Information regarding DS 2 , such as related to the configuration of DS 2 , the performance or storage tier classification for PDs providing storage for LUNs R 1  and R 2  of DS 2 , and the like, may not be available to DS 1 . Since such information is not provided to DS 1 , an embodiment may utilize the techniques herein to discover such information regarding DS 2  and the PDs of DS  2  providing storage for the LUNs of DS 2 . The techniques herein may be performed by executing code on DS 1  to determine such information including performance classifications or storage tiers of underlying PDs providing storage for LUNs R 1  and R 2 . The foregoing information may be used as described elsewhere herein in connection with optimizations whereby an optimizer of DS 1  does data movement and placement of LUN data of LUNs A-C. For example, DS 1  may control movement and placement of data for LUNs B and C on selected ones of LUNs R 1 , R 2  of DS 2  based on particular storage tier classifications of PD groups for LUNs R 1  and R 2 . For example, LUN R 1  of DS 2  may be classified as having its data stored on a first group of PDs which are EFDs and LUN R 2  may be classified as having its data stored on a second group of PDs which are rotating FC disk drives. At a first point in time, DS 1  may store data of LUN B which is frequently accessed by the host on LUN R 1  and may store data of LUN C which is much less frequently accessed by the host on LUN R 2  as illustrated in the example  2800 . At a second point in time, the optimizer may determine that the data of LUN B is now much less frequently accessed than the data of LUN C and may relocate or move data of LUN B to LUN R 2  and may move data of LUN C to LUN R 1 . Thus, DS 1  may address each LUN of DS 2  in a manner similar to one of its own PDs for placement and data movement optimizations. 
     In connection with  FIG. 26 , a storage tier may be specified as a target for migration whereby the PDs of the storage tier may be located in DS 1   2804 . Alternatively, DS 2   2806  may comprise PDs of a storage tier specified as the target storage tier for the migration. 
     It should be noted that the foregoing example describes performing data storage optimizations, such as data movement and placement, with reference to an entire LUN. However, as described elsewhere herein and also appreciated by those skilled in the art, such data movement and placement may be performed with respect to varying and different levels of storage granularity rather than per LUN. For example, the foregoing may be used in connection with data movement and placement for a portion of a LUN such as LUN B whereby a first very active portion of LUN B may have its data stored on LUN R 1  and a second much less active portion of LUN B may have its data stored on LUN R 2  and yet a third portion of LUN B may have its data stored on PDs of DS 1 . DS 1  may then perform processing to appropriately and suitably move such data portions as workload and activity for each such portion may change over time. 
     With reference now to  FIG. 27 , shown is another example of another embodiment that may utilize the techniques herein. The example  2900  includes a host  2902 , appliance  2904  and DS 1   2906  and DS 2   2908 . The appliance  2904  may be a data storage virtualization appliance such as an EMC® VPLEX™ appliance which accesses and aggregates storage from multiple data storage systems DS 1  and DS 2  whereby each such data storage system may be of the same or different types (e.g., same or different manufacturers as well as different array types that may be from a same manufacturer). In this manner, the appliance  2904  may present storage of DS 1  and DS 2  in an aggregated virtualized environment to the host  2902  and other clients. The host  2902  communicates with the appliance  2904  to access data on any one of the virtualized storage devices LUNs A-C exposed to the client and each such virtualized storage device of the appliance may be mapped to one or more other LUNs R 1 -R 3  of one or more other data storage systems such as DS 1   2906  and DS 2   2908 . To illustrate, the appliance  2904  may expose or present to the host LUNs A-C. As such, the appliance  2904  may want to know information about DS 1   2906 , DS 2   2908  and the underlying PD storage from which LUNs R 1 , R 2 , and R 3  are provisioned in connection with providing data services, performing optimizations such as data movement as described elsewhere herein, and the like. Such information regarding the configuration and performance classification of LUNs R 1 , R 2  and R 3  may not be available to the appliance  2904  (e.g. the appliance may not be provided with information regarding the storage tier classification of the underlying PDs from which storage is configured for storing data of LUNs R 1 , R 2 , and R 3 ). Since such information is not provided to the appliance, an embodiment may utilize the techniques herein to discover such information regarding the DSs  2906 ,  2908  and the PDs provisioned for LUNs R 1 -R 3  of DS 1  and DS 2 . The techniques herein may be performed by executing code on the appliance to determine information such as storage classifications of underlying PDs providing storage for LUNs R 1 , R 2  and R 3 . The foregoing information may be used as described elsewhere herein in connection with optimizations whereby an optimizer of the appliance may perform data movement of LUN data of LUNs A-C. For example, the appliance may at a first point in time map LUN A, or a portion thereof, to LUN R 1  of DS 1  determined to have its data stored on PDs of the EFD storage tier. Such mapping may be performed when the host is frequently accessing LUN A&#39;s data (e.g., when LUN A, or portion thereof, has a high workload and the optimizer determines to place such a highly active portion of data on the highest EFD storage tier). At a later point in time, assume the workload to LUN A, or the portion thereof, mapped to the LUN R 1  of DS 1  greatly decreases. The appliance may now determine that LUN A, or the portion thereof, has its data relocated or moved from the EFD storage tier of LUN R 1  to LUN R 2  of DS 2  (whereby LUN R 2  is determined to be of a lower performing tier than EFD (e.g., LUN R 2  may be classified as having underlying PDs which are FC or SATA rotational disk drives rather than EFD drives). 
     In connection with  FIG. 27 , a storage tier may be specified as a target for migration whereby the PDs of the storage tier may be located in the appliance  2904 , DS 1   2906  and/or DS 2   2908 . 
     The foregoing as well as other aspects of data storage system optimizations are described, for example, in U.S. patent application Ser. No. 13/466,775, filed May 8, 2012, PERFORMING DATA STORAGE OPTIMIZATIONS ACROSS MULTIPLE DATA STORAGE SYSTEMS, and U.S. patent application Ser. No. 13/538,245, filed Jun. 29, 2012, DATA STORAGE SYSTEM MODELING, both of which are incorporated by reference herein. 
     The techniques herein may be performed by executing code which is stored on any one or more different forms of computer-readable media. Computer-readable media may include different forms of volatile (e.g., RAM) and non-volatile (e.g., ROM, flash memory, magnetic or optical disks, or tape) storage which may be removable or non-removable. 
     While the invention has been disclosed in connection with preferred embodiments shown and described in detail, their modifications and improvements thereon will become readily apparent to those skilled in the art. Accordingly, the spirit and scope of the present invention should be limited only by the following claims.