Patent Publication Number: US-2023153217-A1

Title: Methods and systems for managing networked storage system resources

Description:
RELATED APPLICATIONS 
     This application claims priority to and is a continuation of U.S. application Ser. No. 16/562,713, file on Sep. 6, 2019, now allowed, titled “METHODS AND SYSTEMS FOR MANAGING NETWORKED STORAGE SYSTEM RESOURCES,” which claims priority to and is a continuation of U.S. Pat. No. 10,409,702, file on Mar. 20, 2017, titled “METHODS AND SYSTEMS FOR MANAGING NETWORKED STORAGE SYSTEM RESOURCES,” which are incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to networked storage environments and more particularly, to innovative computing technology for monitoring and managing resources used by the networked storage environments for storing and retrieving electronic data. 
     BACKGROUND 
     Various forms of storage systems are used today. These forms include direct attached storage (DAS) network attached storage (NAS) systems, storage area networks (SANs), and others. Networked storage systems are commonly used for a variety of purposes, such as providing multiple users with access to shared data, backing up data and others. A networked storage system typically includes at least one computing device executing a storage operating system for storing and retrieving data on behalf of one or more client computing systems (“clients”). The storage operating system stores and manages shared data containers in a set of mass storage devices. 
     Networked storage systems are used extensively in NAS, SAN, cloud based and virtual storage environments. The infrastructure uses various physical and virtual components, for example, servers, switches, host bus adapters, network interface cards, storage devices, volumes, virtual machines and others. The performance and usage of these resources impact the overall performance providing storage services to clients. Continuous efforts are being made to develop computing technology that can be deployed at data centers and networked storage environments to efficiently manage and monitor infrastructure resources. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing features and other features will now be described with reference to the drawings of the various aspects. In the drawings, the same components have the same reference numerals. The illustrated aspects are intended to illustrate, but not to limit the present disclosure. The drawings include the following Figures: 
         FIG.  1 A  shows an example of an operating environment for the various aspects of the present disclosure; 
         FIG.  1 B  shows an example of a management system, according to one aspect of the present disclosure; 
         FIG.  1 C  shows an example of a format for monitoring storage system resources using structured and unstructured data objects in a same format, according to one aspect of the present disclosure; 
         FIG.  1 D  shows an example of a plurality of infrastructure objects that are monitored by the management system of  FIG.  1 B  using the format of  FIG.  1 C , according to one aspect of the present disclosure; 
         FIG.  1 E  shows a format for managing performance data in a networked storage environment, according to one aspect of the present disclosure; 
         FIG.  1 F  shows an example of a plurality of structured resource objects, according to one aspect of the present disclosure; 
         FIG.  1 G  shows an example of different counters that may be used to collect resource performance data for different resource types, according to one aspect of the present disclosure; 
         FIG.  1 H  shows a process for generating a logical index for both structured and unstructured data objects, according to one aspect of the present disclosure; 
         FIG.  1 I  shows a process for using a logical index generated by the innovative computing technology, according to one aspect of the present disclosure. 
         FIG.  1 J  shows an example of a screenshot for executing a user query, according to one aspect of the present disclosure; 
         FIG.  1 K  shows another example of a screenshot for executing a user query, according to one aspect of the present disclosure; 
         FIG.  1 L  shows yet another example of a screenshot for executing a user query, according to one aspect of the present disclosure; 
         FIG.  2 A  shows an example of a clustered storage system, according to one aspect of the present disclosure; 
         FIG.  2 B  shows an example of a storage system node, used according to one aspect of the present disclosure; 
         FIG.  3    shows an example of a storage operating system, used according to one aspect of the present disclosure; and 
         FIG.  4    shows an example of a processing system, according to one aspect of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     As preliminary note, the terms “component”, “module”, “system,” and the like as used herein are intended to refer to a computer-related entity, either software-executing hardware based purpose processor, hardware, firmware and a combination thereof. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. 
     By way of illustration, both an application running on a server and the server can be a component. One or more components may reside within a process and/or thread of execution, and a component may be localized on one computer and/or distributed between two or more computers. Also, these components can execute from various non-transitory, computer readable media having various data structures stored thereon. The components may communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal). 
     Computer executable components can be stored, for example, on non-transitory, computer readable media including, but not limited to, an ASIC (application specific integrated circuit), CD (compact disc), DVD (digital video disk), ROM (read only memory), floppy disk, hard disk, EEPROM (electrically erasable programmable read only memory), memory stick or any other storage device type, in accordance with the claimed subject matter. 
     In one aspect, innovative technology is provided that improves computing technology used for monitoring and managing complex data centers and data center resources. Prior to the described innovative technology, management systems collected performance and configuration data from storage devices, computing devices, switches, adapters and other data center resources, referred to as data sources. The collected data was typically stored as a structured data object having a specific schema and layout. The fixed schema however does not meet all user/client requirements, especially, when users continue to demand information regarding more devices, counters and varying attribute information. The fixed schema format hence has limitations. 
     The innovative technology of this disclosure provides an application programming interface (API) to receive any user specific data, referred to as unstructured data. The unstructured data is interpreted and then transformed into a logical index described below in detail that can be used to respond to client system requests. The logical index format is the same for both structured and unstructured data objects, making the disclosed system flexible and versatile. 
     System  100 :  FIG.  1 A  shows an example of a networked storage operating environment  100  (also referred to as system  100 ) having a plurality of resources for storing and accessing data in a networked storage system in one aspect of the present disclosure. As an example, system  100  may include a plurality of computing systems  104 A- 104 N (may also be referred to and also shown as server system  104  or as host system  104 ) that may access one or more storage systems  108  via a connection system  116  such as a local area network (LAN), wide area network (WAN), the Internet and others. The server systems  104  may communicate with each other via connection system  116 , for example, for working collectively to provide data-access service to user consoles  102 A- 102 N (may be referred to as user  102 ) and/or to host systems  104 . 
     In one aspect, in a SAN environment, one or more switch  120  may be used for communication between server systems  104  and storage systems  108 /storage device(s)  114 . The switch  120  may include a plurality of ports  122 A- 122 B and  124 A- 124 B, having logic and circuitry for handling network packets. For example, port  122 A is coupled to host  104 , port  1228  is coupled to network  116  having other switches, and ports  124 A and  1248  are coupled to storage system  108  and storage device  114 , respectively. 
     Server systems  104  may be computing devices configured to execute applications  106  over a variety of operating systems, including the UNIX® and Microsoft Windows® operating systems. Applications  106 A- 106 N (referred to as application  106 ) may utilize data services of storage system  108  to access, store, and manage data in a set of storage devices  110 / 114  that are described below in detail. Application  106  may include an email exchange application, a database application or any other type of application. In another aspect, application  106  may comprise a virtual machine also described below in more detail. 
     Server systems  104  generally utilize file-based access protocols when accessing information (in the form of files and directories) over a network attached storage (NAS)-based network. Alternatively, server systems  104  may use block-based access protocols, for example, the Small Computer Systems Interface (SCSI) protocol encapsulated over TCP (iSCSI) and SCSI encapsulated over Fibre Channel (FCP) to access storage via a storage area network (SAN). 
     Server  104  may also execute a virtual machine environment  105 , according to one aspect. In the virtual machine environment  105  a physical resource is time-shared among a plurality of independently operating processor executable virtual machines (VMs). Each VM may function as a self-contained platform, running its own operating system (OS) and computer executable, application software. The computer executable instructions running in a VM may be collectively referred to herein as “guest software”. In addition, resources available within the VM may be referred to herein as “guest resources”. 
     The guest software expects to operate as if it were running on a dedicated computer rather than in a VM. That is, the guest software expects to control various events and have access to hardware resources on a physical computing system (may also be referred to as a host platform) which maybe referred to herein as “host hardware resources”. The host hardware resource may include one or more processors, resources resident on the processors (e.g., control registers, caches and others), memory (instructions residing in memory, e.g., descriptor tables), and other resources (e.g., input/output devices, host attached storage, network attached storage or other like storage) that reside in a physical machine or are coupled to the host platform. 
     The virtual execution environment  105  executes a plurality of VMs  126 A- 126 N. VMs  126 A- 126 A execute a plurality of guest OS  128 A- 128 N (may also be referred to as guest OS  128 ) that share hardware resources  134 . As described above, hardware resources  134  may include CPU, memory, I/O devices, storage or any other hardware resource. 
     A virtual machine monitor (VMM)  130 , for example, a processor executed hypervisor layer provided by VMWare Inc., Hyper-V layer provided by Microsoft Corporation (without derogation of any third party trademark rights) or any other layer type, presents and manages the plurality of guest OS  128 A- 128 N. VMM  130  may include or interface with a virtualization layer (VIL)  132  that provides one or more virtualized hardware resource  134  to each guest OS. For example, VIL  132  presents physical storage at storage devices  110 / 114  as virtual storage (for example, as a virtual hard drive (VHD)) to VMs  126 A- 126 N. The VMs use the VHDs to store information at storage devices  110  and  114 . 
     In one aspect, VMM  130  is executed by server system  104  with VMs  126 A- 126 N. In another aspect, VMM  130  may be executed by an independent stand-alone computing system, often referred to as a hypervisor server or VMM server and VMs  126 A- 126 N are presented via another computing system. It is noteworthy that various vendors provide virtualization environments, for example, VMware Corporation, Microsoft Corporation (without derogation of any third party trademark rights) and others. The generic virtualization environment described above with respect to  FIG.  1 A  may be customized depending on the virtual environment provider. 
     System  100  may also include a management system  118  for managing and configuring various elements of system  100 . Management system  118  may include one or more computing systems for performing various tasks described below in detail. Details regarding management system  118  are provided below in more detail. 
     System  100  may also include one or more user consoles  102 A- 102 N referred to as users. Users&#39;  102 A- 102 N may access server system  104  for storage related services provided by storage system  108  and also use management system  118  for obtaining management related services described below in detail. 
     In one aspect, storage system  108  has access to a set of mass storage devices  110  (may be referred to as storage devices  110 ) within a storage subsystem  112 . Storage system  108  may also access storage devices  114  via switch  120  that may be a Fibre Channel, Fibre Channel over Ethernet or any other type of switch. Storage devices  110  and  114  are referenced interchangeably throughout this specification. As an example, storage devices  110  and  114  may be a part of a storage array within the storage sub-system. 
     Storage devices  110 / 114  are used by storage system  108  for storing information. The storage devices  110 / 114  may include writable storage device media such as magnetic disks, video tape, optical, DVD, magnetic tape, non-volatile memory devices for example, self-encrypting drives, flash memory devices and any other similar media adapted to store information. The storage devices  110 / 114  may be organized as one or more groups of Redundant Array of Independent (or Inexpensive) Disks (RAID). The aspects disclosed herein are not limited to any particular storage device or storage device configuration. 
     In one aspect, to facilitate access to storage devices  110 / 114 , a storage operating system of storage system  108  “virtualizes” the storage space provided by storage devices  110 / 114 . The storage system  108  can present or export data stored at storage devices  110 / 114  to server systems  104  and VMM  130  as a storage volume or one or more qtree sub-volume units. Each storage volume may be configured to store data files (or data containers or data objects), scripts, word processing documents, executable programs, and any other type of structured or unstructured data. From the perspective of the VMs/server systems, each volume can appear to be a single disk drive. However, each volume can represent the storage space in one disk, an aggregate of some or all of the storage space in multiple disks, a RAID group, or any other suitable set of storage space. 
     It is noteworthy that the term “disk” as used herein is intended to mean any storage device/space and not to limit the adaptive aspects to any particular type of storage device, for example, hard disks. 
     The storage system  108  may be used to store and manage information at storage devices  110 / 114  based on a request generated by server system  104 , management system  118 , user  102  and/or a VM. The request may be based on file-based access protocols, for example, the CIFS or the NFS protocol, over TCP/IP. Alternatively, the request may use block-based access protocols, for example, iSCSI or FCP. 
     As an example, in a typical mode of operation, server system  104  (or VMs  126 A- 126 N) transmits one or more input/output (I/O) commands, such as an NFS or CIFS request, over connection system  116  to the storage system  108 . Storage system  108  receives the request, issues one or more I/O commands to storage devices  110 / 114  to read or write the data on behalf of the server system  104 , and issues an NFS or CIFS response containing the requested data over the connection system  116  to the respective server system  104   
     In one aspect, storage system  108  may have a distributed architecture, for example, a cluster based system that may include a separate network module and storage module, described below in detail with respect to  FIG.  2 A . Briefly, the network module is used to communicate with host platform server system  104  and management system  118 , while the storage module is used to communicate with the storage devices  110 / 114  that are a part of a storage sub-system. 
     Storage system  108  maintains various data structures for storing information related to storage devices  110 / 114 . For example, storage system  108  is aware of the identity and capabilities of storage device  110 / 114 . Storage system  108  maintains the information regarding all the VMs and server systems that use storage device  110 / 114 . This information may be kept as unique identifiers. 
     Because storage system  108  services read and write requests, it maintains information regarding the number of I/O operations that are processed within a time unit, for example, a second, referred to herein as “IOPS” by the storage device and by each storage volume. Storage system  108  is also aware of the identity of the server systems that generate the I/O requests. Storage system  108  also maintains information on a rate at which information is transferred (also referred to as a throughput rate) from the storage devices. The throughput rate is maintained for each storage volume of the storage devices. 
     Management System  118 :  FIG.  1 B  shows a block diagram of management system  118  having a plurality of modules, according to one aspect. The various modules may be implemented in one computing system at a management server or in a distributed environment among multiple computing systems. In the illustrated aspect, the management system  118  includes a graphical user interface (GUI) module  136  to generate a GUI for use by a storage administrator or a user using a user console  102 . In another aspect, management system  118  may present a command line interface (CLI) to a user. 
     Management system  118  may include a communication module  146  that implements one or more communication protocols (Ethernet, Fibre Channel, InfiniBand and others) and/or APIs to enable the various modules of management system  118  to communicate with the storage system  108 , VMs  126 A- 126 N, server system  104  and clients  102 . 
     In one aspect, management system  118  also includes an acquisition module  144  that obtains information regarding storage devices  110 / 114  from storage system  108  and other resources of system  100 . Acquisition module  144  may send a discovery request to obtain configuration/performance information. The format and structure of the discovery request will depend on the protocol/standard used by acquisition module  144  to communicate with storage system  108  and switch  120 . 
     The configuration/information may include an amount of data that is transferred to and from a storage device within a certain duration, a number of IOPS that are serviced by a storage device, the identity of the server systems (also referred to as host systems) that use the storage devices, transfer rates of the switch ports, utilization of the storage devices, storage nodes, cache utilization of the storage nodes, cache hit ratio of the storage nodes, and other information. 
     Management system  118  also includes a processor executable configuration module  142  that stores configuration information  148  for various resources used by system  100 , for example, storage system nodes, storage devices, switches and other resources. The configuration information may be stored as data structure  148 . 
     As an example, management system  118  maintains information regarding storage device  110  and  114  at resource data structure  148  to store a name of a storage device manufacturer, a storage device identifier, a maximum number of IOPS that the device can handle and a throughput rate that the storage device is able to support. 
     Resource configuration data  148  also identifies the storage system  108  that manages a storage device, the storage volumes associated with the storage device and the identity of users (for example, server systems  104 ) that access the storage volumes. This information may be obtained from storage system  108 . 
     Resource configuration data  148 , may also identify the switch  120  used by system  100 , the various ports of switch and the identity of the devices/computing systems that are coupled to the switch. This information is acquired by acquisition module  144  either directly from the switch or any other entity, according to one aspect. 
     Resource configuration data  148  may also identify the VMM  130 , for example, the hypervisor that presents and controls VMs  126 A- 126 N; the various VMs and the resources that are used by the VMs at any given time, for example, VHDs. This information may also be acquired by acquisition module  144  from VMM  130  and storage system  108 . 
     Management system  118  includes a performance monitoring module (may be referred to as performance module)  140  that receives performance data regarding various resources of system  100 . The performance data may be collected based on stored policies  154 . The resource performance data may be stored at a data structure  150 . The performance data  150  shows if a storage device is over utilized at a given time, the number of IOPS within certain duration, a throughput within the certain duration, available capacity at any given time and other information. 
     Performance data  150  further includes information regarding switch performance, Node CPUs and any other configured resource. The performance data  150  may also store information indicating the current utilization and available performance capacity of the resource at any given time. Performance data  150  may also include information regarding the various VMs, identity of the virtual disks used by the VMs and other information that is described below in more detail. It is noteworthy that performance data  150  may be stored as part of structured and unstructured data objects described below in detail. 
     The management system  118  presents an unstructured data API  107  (may also be referred to as API  107 ) to collect information regarding unstructured data objects  109 . The management system  118  maintains data for both structured data objects  111  and unstructured data objects  109  in the same format, as described below in detail. It is noteworthy that structured data objects and unstructured data objects  109  may include a subset of configuration and performance information that are shown separately as  148  and  150  in  FIG.  1 B  for convenience and described above in detail. 
     Management system  118  may also include other modules  138 . The other modules  138  are not described in detail because the details are not germane to the inventive aspects. 
       FIG.  1 C  shows a novel format generated and used by the innovative computing technology disclosed herein for improving manageability of data center resources, according to one aspect of the present disclosure. The novel format discloses a logical index  113 A for a structured data object  111  and logical index  113 B for unstructured data object  109  described below in detail. 
     The term structured data object as used herein means data objects representing resources. The structured data objects store and manage the performance data based on a fixed schema/relationship (for example, a MySQL database format (without derogation of any third party trademark rights). The term unstructured data object  109  includes unstructured data that does not comply with any specific schema or format and is collected by the unstructured API  107 , an example of which is shown below. The unstructured data is stored and processed similar to the structured data. 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 [ 
               
               
                   
                  { 
               
               
                   
                   “identifiers”: { 
               
               
                   
                    “id1”: “value1”, 
               
               
                   
                    “id2”: “value2”, 
               
               
                   
                    ... 
               
               
                   
                    “idN”: “valueN” 
               
               
                   
                   }, 
               
               
                   
                   “attributes”: { 
               
               
                   
                    “attr1”: “attrVal1”, 
               
               
                   
                    “attr2”: “attrVal2”, 
               
               
                   
                    ... 
               
               
                   
                    “attrM”: “attrValM” 
               
               
                   
                   }, 
               
               
                   
                   “dataPoints”: { 
               
               
                   
                    “sampleTimeUTC”: &lt;UNIX time milliseconds&gt;, 
               
               
                   
                    “counter1”: counterVal1, 
               
               
                   
                    “counter2”: counterVal2, 
               
               
                   
                    ... 
               
               
                   
                    “counterX”: counterValX 
               
               
                   
                   } 
               
               
                   
                  }, 
               
               
                   
                  ... other entries ... 
               
               
                   
                 ] 
               
               
                   
                   
               
            
           
         
       
     
     API  107  shown above, receives unstructured data for unstructured data object  109 . API  107  extracts one or more unique identifiers (shown above as id1, id2 . . . idN) from the unstructured data. The identifiers vary based on the data type and the resource type. For example, the identifiers may include an Internet Protocol address, Media Access Control (MAC) address or any other field that can be used to uniquely identify the unstructured data in a specific domain. The attributes associated with the data are included as “attr1”, “attr2” and the attribute values may change. For example, an attribute of a switch port may be 1G or 10G indicating a port or a network link&#39;s operating speed. 
     API  107  also includes data points that identify a data collection schedule and the different counters that may be needed to track and collect the data associated with the unstructured data object  109 . 
     The logical index  113 A for the structured data object  111  includes a configuration index  111 A and a performance index  111 B. As an example, logical index  113 A may be used to represent a traditional schema element, for example, a storage volume that is identified by a unique volume identifier. The configuration index  111 A provides a single configuration file for one storage element. As an example, for each storage volume, there is a single file for the configuration index  111 A. The configuration index stores key value pairs, for example, (Volume name, Space), (volume name, capacity), (volume name, usage) and others. The key value pairs will vary based on the type of structured objects. 
     Performance index  111 B for the structure data object  111  is used to store performance related information in a series of indexes, for example, 1 per day. The configuration and performance index are associated with each other using an object identifier for the traditional data element. Using the logical index  113 A, the management system  118  is able to retrieve and provide data regarding traditional objects as a group, for example, “volumes”. 
     The logical index  113 B is similar to the logical index  113 , except the data is unstructured and can be used to define new and ad-hoc objects that do not need to conform to any specific schema. The logical index  113 B uses an identifier hash  115  (shown as ID hash  115 ] to link the configuration index  109 A and the performance index  109 B. The ID hash is generated based one or more identifiers (shown as Id1, Id2 . . . idN in the API  107  example above). The ID hash  115  is flexible and unique to each new object type. It is different from an object ID that is predefined for a structured data object  111 . By generating the ID hash  115  in real-time, any new unstructured object type can be handled using the same mechanisms that are used for processing structured data types. This is significantly different from traditional management system computing technology that are reliant on fixed database schemas/formats. 
     Once the structured and unstructured objects are generated, GUI widgets and custom user dashboards can built natural language queries to obtain performance and resource information so that storage environment resources can be optimally allocated for processing input/output requests for storing electronic data. Examples of using the format of  FIG.  1 C  are provided in  FIGS.  1 J- 1 L  and described below in detail. 
       FIG.  1 D  shows an example of how performance data is maintained and collected for various resources represented by structured and unstructured objects, according to one aspect. As an example, there are may be two types of resources, a service center and a delay center resource. The service center is a resource category that can be represented by a queue with a wait time and a service time (for example, a processor that processes a request out of a queue). The delay center may be a logical representation for a control point where a request stalls waiting for a certain event to occur and hence the delay center represents the delay in request processing. The delay center may be represented by a queue that does not include service time and instead only represents wait time. The distinction between the two resource types is that for a service center, performance data may include a number of visits, wait time per visit and service time per visit. For the delay center, only the number of visits and the wait time per visit at the delay center. 
     In one aspect, a flow type i.e. a logical view of the resources is used for handling client requests. The flow types include two categories, latency and utilization. A latency flow type is used for analyzing how long operations take at the service and delay centers. The latency flow type is used to identify a workload whose latency has increased beyond a certain level. A typical latency flow may involve writing data to a storage device based on a client request and there is latency involved in writing the data at the storage device. The utilization flow type is used to understand resource consumption of workloads and may be used to identify resource contention. 
     The various resources of system  100  are represented logically as infrastructure objects  156 A- 156 N (may also be referred to as resource objects  156 ). Data associated with the resources is collected using various counters shown as  158 A- 158 N and  160 A- 160 N and then stored at performance data structure  150  ( FIG.  1 B ). In one aspect, the performance data  150  is stored as part of performance index  109 B and  111 B. Each counter is used to collect certain performance metrics, for example, latency, throughput, utilization, the number IOPS and others. The adaptive aspects disclosed herein are not limited to any specific performance metric. 
       FIG.  1 E  shows an example of how a policy maybe associated with an infrastructure object  156  for collecting performance data, according to one aspect of the present disclosure. Infrastructure object  156  may be associated with one or more policies  162 A- 162 N and a time window  170 . The policies may be for structured objects and unstructured objects defined by the data received in API  107  described above in detail. Threshold values  172  are assigned to certain parameters for generating alerts and severity  174  defines the importance of an alert, for example, an alert may be critical, or it may only be a warning. Based on the policy, counters  156 A are used to collect the appropriate data for the time window  170 . 
     Object Hierarchy:  FIG.  1 F  shows an example of a format  151  for structured objects  111  for tracking information/relationships regarding different resources that are used within storage system  100  and a clustered storage system shown in  FIG.  2 A  and described below in detail. Each resource is represented as an object and is identified by a unique identifier value (object ID) that is used for logical index  113 A. One or more counters collect performance data associated with the resource for the performance index  111 B, described above in detail. 
     Format  151  maybe a hierarchical mesh where various objects may have parent-child, peer and remote peer relationships, as described below. As an example, format  151  shows a cluster object  151 A that may be categorized as a root object type for tracking storage cluster ( 202 ,  FIG.  2 A ) level resources. The cluster object  151 A is associated with various child objects, for example, a storage node object  152 B that identifies a storage node within the cluster. The cluster object  151 A stores information regarding the cluster, for example, the number of nodes it may have, information identifying the nodes; and any other information. 
     The storage node object  151 B stores information regarding a node, for example, a node identifier and performance data regarding the nodes, for example, CPU utilization of the nodes, latency (i.e. delay) in processing I/O requests, the number of storage volumes the node is managing, cache utilization, cache hit ratio and other information. 
     Each cluster node object  151 B may be associated with other objects for example, a storage pool  151 E and a port object  151 D that is a child of a switch object  151 C. The port object  151 D is also associated with a storage device object  151 G denoting that the port provides access to the storage device. 
     The storage pool  151 E object stores an identifier for identifying a storage pool that may have one or more aggregates associated with one or more storage devices. The storage pool object  151 E stores information regarding storage utilization, latency in responding to I/O requests and other information by one or more storage pools. 
     The storage pool  151 E is associated with an internal volume object  151 H that is managed by the storage operating system. The internal volume is associated with a Qtree object  151 I that in turn is associated with a volume (for example, a LUN)  151 M that is presented to a host system or a share (for example, a CIFS share)  151 N. The volume  151 M may be associated with a data store  151 L. 
     A host system object  151 F is used to store information regarding a host and a virtual machine  151 J tracks performance/configuration information regarding a virtual machine. The virtual disk object  151 K is used to track information regarding a virtual disk. The virtual disk object  151 K is also associated with the data store object  151 L. 
     The various objects of  FIG.  1 E  are shown as an example. Other object types may be added based on an operating environment. The performance data and the configuration data including the relationship information between the resources is stored at a storage device, as described below in detail. 
       FIG.  1 G  shows an example of various structured objects  111 , according to one aspect of the present disclosure. For example, infrastructure structured objects include a data store object  174  with associated data store policies  174 A and counters  1748 . The data store object  174  is used to track a plurality of virtual disks (VMDKs) that may be used within a VM for storing information. 
     Structured objects may include a storage device object  176  with storage device policies  176 A and counters  1768 . The storage device object  176  is used for tracking attributes of different storage devices using counters  1768 . 
     A hypervisor (or VMM) object  178  with policies  178 A and counters  178 B is used for tracking attributes of the hypervisor using counters  1788 . A volume object  180  with policies  180 A and counters  1808  is used for tracking attributes of a volume using counters  1808 . The volume object  180  represents a volume that is presented to a host system for storing data. 
     A storage node object  182  with policies  182 A and counters  182 B is used for tracking attributes of a storage node using counters  1828 , for example node CPU utilization, cache hit ratio, cache utilization, available capacity of a storage node for handling a new workload and other attributes. 
     A storage array object  184  with policies  184 A and counters  1848  is used for tracking attributes of a storage array using counters  1848  including used capacity at any given time, available capacity and other attributes. 
     A storage pool object  186  with policies  186 A and counters  186 B is used for tracking attributes of a storage pool (for example, an aggregate having a plurality of storage devices) using counters  1868 . 
     A virtual machine object  190  with policies  190 A and counters  190 B is used for tracking attributes of a VM using counters  190 B. A virtual disk object (VMDK)  188  with policies  188 A and counters  188 B is used for tracking attributes of a VMDK using counters  188 B. 
     An internal volume object  193  with policies  193 A and counters  193 B is used for tracking attributes of an internal volume using counters  193 B. An internal volume is a logical representation of storage as maintained by a storage operating system, described below in detail. 
     A port object  195  with associated policies  195 A and counters  195 B is used to track port attributes. The ports are used to receive and send information by the storage nodes and the host systems. 
     A host system object  197  with associated policies  197 A and counters  197 B is used to represent host computing systems, for example,  104 . 
     Table I below shows an example of various counters/metrics associated with a subset of structured objects  111  (for example, Storage, Storage_Node and Storage Pool) of  FIG.  1 F  that are maintained by the management  118 , according to one aspect. The Column Labelled “Object” identifies an infrastructure, structured object that is monitored and tracked. The second column shows the “Counter” (or metric) associated with the infrastructure object. The third column shows the unit associated with the performance metric. For example, the unit MBS means, megabytes per second, IOPS means number of I/O (i.e. read and/or write) operations per second, and the other units that are self-explanatory. The fourth column provides a description of the performance data that is being collected for an object/counter. 
     Briefly, the “Storage” object of Table I is the storage device where data will be stored for a new workload, the object “Storage_Node” is a compute node for an array/cluster that manages data flow to storage devices and the object “Storage_Pool” is a logical pool of storage devices in a storage array that comprises of various storage nodes and storage devices. The term port in Table I below may include an inter-connect switch port that routes traffic between storage nodes as well as the adapter ports used by the storage nodes. It is noteworthy that Table I is only an example, other structured objects of  FIG.  1 F  are also tracked and can be used for implementing the adaptive aspects of the present disclosure. 
     
       
         
           
               
               
               
               
             
               
                 TABLE I 
               
               
                   
               
               
                 Object 
                 Counter(s)/Metrics 
                 Unit 
                 Description 
               
               
                   
               
             
            
               
                 Storage 
                 Total Throughput 
                 MBS 
                 Total data written to the object 
               
               
                 Storage 
                 Total Maximum 
                 MBS 
                 Maximum data read and written to the 
               
               
                   
                 Throughput 
                   
                 object 
               
               
                 Storage 
                 Total IOPS 
                 Number of 
                 Total number of read and write 
               
               
                   
                   
                 IOS 
                 requests per second 
               
               
                 Storage 
                 Total maximum 
                 Number of 
                 Maximum number of read and write 
               
               
                   
                 IOPS 
                 IOS 
                 requests per second 
               
               
                 Storage 
                 Total cache hit ratio 
                 Percentage 
                 Ratio of IO requests served by a 
               
               
                   
                   
                   
                 cache and the storage devices for the 
               
               
                   
                   
                   
                 array 
               
               
                 Storage 
                 Total cache 
                 Percentage 
                 Cache utilization of the array 
               
               
                   
                 utilization 
               
               
                 Storage_Node 
                 Total throughput 
                 MBS 
                 Total data read and written to the 
               
               
                   
                   
                   
                 object 
               
               
                 Storage_Node 
                 Maximum 
                 MBS 
                 Maximum data read and written to the 
               
               
                   
                 throughput 
                   
                 object 
               
               
                 Storage_Node 
                 Total IOPS 
                 Number of 
                 Total number of read and write 
               
               
                   
                   
                 IOS 
                 requests per second for the object 
               
               
                 Storage_Node 
                 Maximum IOPS 
                 Number of 
                 Maximum number of read and write 
               
               
                   
                   
                 IOS 
                 requests per second for the object 
               
               
                 Storage_Node 
                 Total utilization 
                 Percentage 
                 Total Node utilization 
               
               
                 Storage_Node 
                 Maximum utilization 
                 Percentage 
                 Maximum utilization of the devices 
               
               
                   
                   
                   
                 managed by the node 
               
               
                 Storage_Node 
                 Total port utilization 
                 Percentage 
                 Port utilization of the node 
               
               
                 Storage_Node 
                 Total cache hit ratio 
                 Percentage 
                 Ratio of I/O requests served by a 
               
               
                   
                   
                   
                 cache for the node 
               
               
                 Storage_Node 
                 Total port errors 
                 None 
                 The number of port errors in the array 
               
               
                 Storage_Node 
                 Total port traffic 
                 MBS 
                 Total data read and written to the 
               
               
                   
                   
                   
                 object i.e. total data transferred by the 
               
               
                   
                   
                   
                 port 
               
               
                 Storage_Pool 
                 Total utilization 
                 Percentage 
                 Total storage device utilization in the 
               
               
                   
                   
                   
                 storage pool 
               
               
                 Storage_Pool 
                 Maximum utilization 
                 Percentage 
                 Total storage device utilization in the 
               
               
                   
                   
                   
                 storage pool 
               
               
                 Storage_Pool 
                 Total IOPS 
                 Number of 
                 Total number of read and write 
               
               
                   
                   
                 IOS 
                 requests processed per second by the 
               
               
                   
                   
                   
                 storage pool 
               
               
                 Storage_Pool 
                 Maximum IOPS 
                 Number of 
                 Maximum number of read and write 
               
               
                   
                   
                 IOS 
                 requests per second processed by the 
               
               
                   
                   
                   
                 storage pool 
               
               
                 Storage_Pool 
                 Total throughput 
                 MBS 
                 Total data read and written to the 
               
               
                   
                   
                   
                 object 
               
               
                 Storage_Pool 
                 Maximum 
                 MBC 
                 Maximum data read and written to the 
               
               
                   
                 throughput 
                   
                 object 
               
               
                 Storage_Pool 
                 Free Usable 
                 MB 
                 Total storage pool capacity available 
               
               
                   
                 capacity 
                   
                 for new workloads 
               
               
                   
               
            
           
         
       
     
     It is noteworthy that the foregoing data is stored using the logical index  113 A for structured data objects. 
     Process Flow:  FIG.  1 H  shows a novel process  117  executed by the computing technology described herein. The process begins in block B 119 , when management system  118 , a server  104  and a storage system  108  are operational and initialized. 
     In block B 121 , a logical index for each structured data object, for example, the objects shown in the hierarchy  151  are created. Each object is identified by a unique object ID and the logical index has a configuration index (e.g.  111 A,  FIG.  1 C ) and a performance index (e.g.  111 B,  FIG.  1 C ). In one aspect, the logical index for structured objects is different from a traditional fixed schema for the structured data objects conforming to a specific format/hierarchy. 
     In block B 123 , unstructured data is received for an unstructured data object via API  107 . The data in this case does not meet any fixed schema or hierarchy requirements. In block B 125 , an ID hash  115  is determined for the unstructured data. A logical index ( 113 B,  FIG.  1 C ) for the unstructured data object is then created. 
     In block B 127 , the management system  118  maintains the logical index for both structured and unstructured data objects. The only difference being that for an unstructured data object, unlike the object ID of a structured data object, the hash ID of the unstructured data object is generated by the management system  118  based on a value extracted from the unstructured data. 
       FIG.  1 I  shows a process  129  for using the logical index for structured and unstructured data objects, according to one aspect of the present disclosure. The process begins in block B 131  when logical index for both structural and unstructured storage objects have been created and stored at a storage location by management system  118 . In block B 133 , request for information regarding a storage object is received via GUI  136 . The request specifies the type of information that is to be retrieved. The requested information may involve a structured data object, unstructured data object or both. Based on the request, in block B 135 , one or more logical index is accessed for raw information. The information is parsed and pre-processed in block B 137 . The pre-processing type will depend on the nature of the request. Thereafter, the requested information is provided to the user. 
       FIGS.  1 J- 1 L  show example screen shots of using the logical indexes described above in detail. For example,  FIG.  1 J  shows an example of handling a query for providing a visual display after grouping/rolling up data. An example query for  FIG.  1 J  may specify: Object type (e.g. Storage); Counter/Metric (Capacity); Filters (Raw capacity &gt;=10000 GB); Roll up/Grouping (Sum by Vendors); Order/Limit (e.g. Top  10 ); and Time range (Optional to override, default time range is provided by a storage dashboard) 
     To respond to the above query, a storage dashboard is presented by GUI  136 .  FIG.  1 J  shows a first GUI widget  141 A that lists various Vendors (for example, NetApp Inc, Dell, EMC (without derogation of any trademark rights). The table widget also shows the name of the storage devices and the raw capacity. The second widget shown as  141 B is a bar chart providing a listing of the vendors and the raw capacity used by the vendors. 
       FIG.  1 K  uses “vendor” as a variable to obtain and visualize different data sets for storage vendors in a data center using segments  143 A- 143 D. The storage dashboard obtains information regarding different storage devices and provides both graphical and tabular illustration of IOPS, capacity, latency and throughput involving vendor specific devices ( 143 A- 143 D). 
       FIG.  1 L  allows a user to use a time range to compare data for same objects at different times. The screenshot  145  shows storage resource data for vendor NetApp with comparison from a week ago, both in tabular form and graphically. 
     The innovative technology disclosed herein improves computing technology for data centers so that storage administrators can customize queries regardless of how resource performance data is handled. 
     In one aspect, methods and systems for a networked storage environment are provided. One method includes maintaining by a processor of a management console, a plurality of structured objects representing a plurality of resources in a networked storage system for storing and retrieving client data from a plurality of storage devices. Each structured object is identified by a unique object identifier and is managed by a logical index having a configuration index that stores configuration information regarding each structured object and a performance index for storing performance data associated with each structured object. The method further includes receiving by the processor unstructured data from an application programming interface (API) associated with a resource of the networked storage system; parsing by the processor, the unstructured data and generating an identifier hash value based on uniquely identifying fields of the unstructured data for an unstructured object; and generating by the processor a logical index with a configuration index and a performance index for the unstructured object identified by the identifier hash value for responding to user requests for performance information regarding the unstructured object. 
     Clustered Storage System:  FIG.  2 A  depicts an illustrative aspect of a networked storage environment  200  including a plurality of server systems  204 . 1 - 204 . 2  (similar to server systems  104 ), a clustered storage system  202  and at least one computer network  206  communicably connecting the server systems  204 . 1 - 204 . 2  and the clustered storage system  202 . Management system  118  retrieves and analyzes information from various cluster nodes as described above in detail. In particular, storage performance data  150  and configuration data  148  may be obtained from the various cluster nodes. 
     As shown in  FIG.  2 A , the clustered storage system  202  includes a plurality of nodes  208 . 1 - 208 . 3 , a cluster switching fabric  210 , and a plurality of mass storage devices  212 . 1 - 212 . 3  (similar to  110 / 114 ,  FIG.  1 A ). 
     Each of the plurality of nodes  208 . 1 - 208 . 3  are configured to include a network module, a storage module (for example, Storage_Node of Table I), and a management module, each of which can be implemented as a separate processor executable or machine implemented module. Specifically, node  208 . 1  includes a network module  214 . 1 , a storage module 216 . 1 , and a management module 218 . 1 , node  208 . 2  includes a network module  214 . 2 , a storage module  216 . 2 , and a management module 218 . 2 , and node  208 . 3  includes a network module  214 . 3 , a storage module 216 . 3 , and a management module 218 . 3 . 
     The network modules  214 . 1 - 214 . 3  include functionality that enables the respective nodes  208 . 1 - 208 . 3  to connect to one or more of the client systems  204 . 1 - 204 . 2  over the computer network  206 , while the storage modules  216 . 1 - 216 . 3  connect to one or more of the storage devices  212 . 1 - 212 . 3 . 
     The management modules  218 . 1 - 218 . 3  provide management functions within the clustered storage system  202 . Accordingly, each of the plurality of server nodes  208 . 1 - 208 . 3  in the clustered storage server arrangement provides the functionality of a storage server. 
     A switched virtualization layer including a plurality of virtual interfaces (VIFs)  220  is provided below the interface between the respective network modules  214 . 1 - 214 . 3  and the client systems  204 . 1 - 204 . 2 , allowing storage  212 . 1 - 212 . 3  associated with the nodes  208 . 1 - 208 . 3  to be presented to the client systems  204 . 1 - 204 . 2  as a single shared storage pool. For example, the switched virtualization layer may implement a virtual interface architecture.  FIG.  2 A  depicts only the VIFs  220  at the interfaces to the network modules  214 . 1 ,  214 . 3  for clarity of illustration. 
     The clustered storage system  202  can be organized into any suitable number of virtual servers (VServers or storage virtual machines (SVMs))  222 A- 222 N, in which each virtual storage system represents a single storage system namespace with separate network access. Each virtual storage system has a user domain and a security domain that are separate from the user and security domains of other virtual storage systems. Server systems  204  can access storage space via a VServer from any node of the clustered system  202 . 
     Each of the nodes  208 . 1 - 208 . 3  may be defined as a computer adapted to provide application services to one or more of the client systems  204 . 1 - 204 . 2 . In this context, a SVM is an instance of an application service provided to a client system. The nodes  208 . 1 - 208 . 3  are interconnected by the switching fabric  210 , which, for example, may be embodied as a Gigabit Ethernet switch or any other switch type. 
     Although  FIG.  2 A  depicts three network modules  214 . 1 - 214 . 3 , the storage modules  216 . 1 - 216 . 3 , and the management modules  218 . 1 - 218 . 3 , any other suitable number of network modules, storage modules, and management modules may be provided. There may also be different numbers of network modules, storage modules, and/or management modules within the clustered storage system  202 . For example, in alternative aspects, the clustered storage system  202  may include a plurality of network modules and a plurality of storage modules interconnected in a configuration that does not reflect a one-to-one correspondence between the network modules and storage modules. 
     The server systems  204 . 1 - 204 . 2  (similar to host  104 ) of  FIG.  2 A  may be implemented as computing devices configured to interact with the respective nodes  208 . 1 - 208 . 3  in accordance with a client/server model of information delivery. In the presently disclosed aspect, the interaction between the server systems  204 . 1 - 204 . 2  and the nodes  208 . 1 - 208 . 3  enable the provision of network data storage services. Specifically, each server system  204 . 1 ,  204 . 2  may request the services of one of the respective nodes  208 . 1 ,  208 . 2 ,  208 . 3 , and that node may return the results of the services requested by the client system by exchanging packets over the computer network  206 , which may be wire-based, optical fiber, wireless, or any other suitable combination thereof. The server systems  204 . 1 - 204 . 2  may issue packets according to file-based access protocols, such as the NFS or CIFS protocol, when accessing information in the form of files and directories. 
     In a typical mode of operation, one of the server systems  204 . 1 - 204 . 2  transmits an NFS or CIFS request for data to one of the nodes  208 . 1 - 208 . 3  within the clustered storage system  202 , and the VIF  220  associated with the respective node receives the client request. It is noted that each VIF  220  within the clustered system  202  is a network endpoint having an associated IP address. The server request typically includes a file handle for a data file stored in a specified volume on at storage  212 . 1 - 212 . 3 . 
     Storage System Node:  FIG.  2 B  is a block diagram of a computing system  224 , according to one aspect. System  224  may be used by a stand-alone storage system  108  and/or a storage system node operating within a cluster based storage system described above with respect to  FIG.  2 A . 
     System  224  may include a plurality of processors  226 A and  226 B, a memory  228 , a network adapter  234 , a cluster access adapter  238  (used for a cluster environment), a storage adapter  240  and local storage  236  interconnected by a system bus  232 . The local storage  236  comprises one or more storage devices, such as disks, utilized by the processors to locally store configuration and other information. 
     The cluster access adapter  238  comprises a plurality of ports adapted to couple system  224  to other nodes of a cluster as described above with respect to  FIG.  2 A . In the illustrative aspect, Ethernet may be used as the clustering protocol and interconnect media, although it will be apparent to those skilled in the art that other types of protocols and interconnects may be utilized within the cluster architecture described herein. 
     System  224  is illustratively embodied as a dual processor storage system executing a storage operating system  230  that preferably implements a high-level module, such as a file system, to logically organize information as a hierarchical structure of named directories, files and special types of files called virtual disks (hereinafter generally “blocks”) on storage devices  110 / 114 / 212 . However, it will be apparent to those of ordinary skill in the art that the system  224  may alternatively comprise a single or more than two processor systems. Illustratively, one processor  226  executes the functions of a network module on a node, while the other processor  226 B executes the functions of a storage module. 
     The memory  228  illustratively comprises storage locations that are addressable by the processors and adapters for storing programmable instructions and data structures. The processor and adapters may, in turn, comprise processing elements and/or logic circuitry configured to execute the programmable instructions and manipulate the data structures. It will be apparent to those skilled in the art that other processing and memory means, including various computer readable media, may be used for storing and executing program instructions described herein. Memory  228  may also be used as a cache for processing I/O requests. 
     The storage operating system  230 , portions of which is typically resident in memory and executed by the processing elements, functionally organizes the system  224  by, inter alia, invoking storage operations in support of the storage service provided by storage system  108 . An example of operating system  230  is the DATA ONTAP® (Registered trademark of NetApp, Inc. operating system available from NetApp, Inc. that implements a Write Anywhere File Layout (WAFL® (Registered trademark of NetApp, Inc.)) file system. However, it is expressly contemplated that any appropriate storage operating system may be enhanced for use in accordance with the inventive principles described herein. As such, where the term “ONTAP” is employed, it should be taken broadly to refer to any storage operating system that is otherwise adaptable to the teachings of this invention. 
     The network adapter  234  comprises a plurality of ports adapted to couple the system  224  to one or more server systems over point-to-point links, wide area networks, virtual private networks implemented over a public network (Internet) or a shared local area network. The network adapter  234  thus may comprise the mechanical, electrical and signaling circuitry needed to connect storage system  108  to the network. Illustratively, the computer network may be embodied as an Ethernet network or a FC network. 
     The storage adapter  240  cooperates with the storage operating system  230  executing on the system  224  to access information requested by the server systems  104  and management system  118  ( FIG.  1 A ). The information may be stored on any type of attached array of writable storage device media such as video tape, optical, DVD, magnetic tape, bubble memory, electronic random access memory, flash memory devices, micro-electro mechanical and any other similar media adapted to store information, including data and parity information. 
     The storage adapter  240  comprises a plurality of ports having input/output (I/O) interface circuitry that couples to the disks over an I/O interconnect arrangement, such as a conventional high-performance, FC link topology. In another aspect, instead of using a separate network and storage adapter, a converged adapter is used to process both network and storage traffic. 
     Operating System:  FIG.  3    illustrates a generic example of operating system  230  executed by storage system  108 , according to one aspect of the present disclosure. Storage operating system  230  interfaces with the management system  118  and provides information for the various data structures maintained by the management system  118 , described above in detail. 
     As an example, operating system  230  may include several modules, or “layers”. These layers include a file system manager  303  that keeps track of a directory structure (hierarchy) of the data stored in storage devices and manages read/write operations, i.e. executes read/write operations on disks in response to server system  104  requests. 
     Operating system  230  may also include a protocol layer  303  and an associated network access layer  305 , to allow system  200  to communicate over a network with other systems, such as server system  104  and management system  118 . Protocol layer  303  may implement one or more of various higher-level network protocols, such as NFS, CIFS, Hypertext Transfer Protocol (HTTP), TCP/IP and others. 
     Network access layer  305  may include one or more drivers, which implement one or more lower-level protocols to communicate over the network, such as Ethernet. Interactions between server systems  104  and mass storage devices  110 / 114 / 212  are illustrated schematically as a path, which illustrates the flow of data through operating system  230 . 
     The operating system  230  may also include a storage access layer  307  and an associated storage driver layer  309  to communicate with a storage device. The storage access layer  307  may implement a higher-level disk storage protocol, such as RAID (redundant array of inexpensive disks), while the storage driver layer  309  may implement a lower-level storage device access protocol, such as FC or SCSI. 
     It should be noted that the software “path” through the operating system layers described above needed to perform data storage access for a client request may alternatively be implemented in hardware. That is, in an alternate aspect of the disclosure, the storage access request data path may be implemented as logic circuitry embodied within a field programmable gate array (FPGA) or an ASIC. This type of hardware implementation increases the performance of the file service provided by storage system  108 . 
     As used herein, the term “storage operating system” generally refers to the computer-executable code operable on a computer to perform a storage function that manages data access and may implement data access semantics of a general purpose operating system. The storage operating system can also be implemented as a microkernel, an application program operating over a general-purpose operating system, such as UNIX® or Windows XP®, or as a general-purpose operating system with configurable functionality, which is configured for storage applications as described herein. 
     In addition, it will be understood to those skilled in the art that the invention described herein may apply to any type of special-purpose (e.g., file server, filer or storage serving appliance) or general-purpose computer, including a standalone computer or portion thereof, embodied as or including a storage system. Moreover, the teachings of this disclosure can be adapted to a variety of storage system architectures including, but not limited to, a network-attached storage environment, a storage area network and a disk assembly directly-attached to a client or host computer. The term “storage system” should therefore be taken broadly to include such arrangements in addition to any subsystems configured to perform a storage function and associated with other equipment or systems. 
     Processing System:  FIG.  4    is a high-level block diagram showing an example of the architecture of a processing system, at a high level, in which executable instructions as described above can be implemented. The processing system  400  can represent modules of management system  118 , user console  102 , server systems  104 , storage system  108  and others. Note that certain standard and well-known components which are not germane to the present invention are not shown in  FIG.  4   . 
     The processing system  400  includes one or more processors  402  and memory  404 , coupled to a bus system  405 . The bus system  405  shown in  FIG.  4    is an abstraction that represents any one or more separate physical buses and/or point-to-point connections, connected by appropriate bridges, adapters and/or controllers. The bus system  405 , therefore, may include, for example, a system bus, a Peripheral Component Interconnect (PCI) bus, a HyperTransport or industry standard architecture (ISA) bus, a small computer system interface (SCSI) bus, a universal serial bus (USB), or an Institute of Electrical and Electronics Engineers (IEEE) standard 1394 bus (sometimes referred to as “Firewire”). 
     The processors  402  are the central processing units (CPUs) of the processing system  400  and, thus, control its overall operation. In certain aspects, the processors  402  accomplish this by executing programmable instructions stored in memory  404 . A processor  402  may be, or may include, one or more programmable general-purpose or special-purpose microprocessors, digital signal processors (DSPs), programmable controllers, application specific integrated circuits (ASICs), programmable logic devices (PLDs), or the like, or a combination of such devices. 
     Memory  404  represents any form of random access memory (RAM), read-only memory (ROM), flash memory, or the like, or a combination of such devices. Memory  404  includes the main memory of the processing system  400 . Instructions  406  which implements techniques introduced above may reside in and may be executed (by processors  402 ) from memory  404 . For example, instructions  406  may include code used by API  107 , performance module  140 , acquisition module  144 , configuration module  142 , GUI  136  as well as instructions for executing the process blocks of  FIGS.  1 H / 1 I. 
     Also connected to the processors  402  through the bus system  405  are one or more internal mass storage devices  410 , and a network adapter  412 . Internal mass storage devices  410  may be or may include any conventional medium for storing large volumes of data in a non-volatile manner, such as one or more magnetic or optical based disks. The network adapter  412  provides the processing system  400  with the ability to communicate with remote devices (e.g., storage servers) over a network and may be, for example, an Ethernet adapter, a FC adapter, or the like. The processing system  400  also includes one or more input/output (I/O) devices  408  coupled to the bus system  405 . The I/O devices  408  may include, for example, a display device, a keyboard, a mouse, etc. 
     Cloud Computing: The system and techniques described above are applicable and useful in the upcoming cloud computing environment. Cloud computing means computing capability that provides an abstraction between the computing resource and its underlying technical architecture (e.g., servers, storage, networks), enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. The term “cloud” is intended to refer to the Internet and cloud computing allows shared resources, for example, software and information to be available, on-demand, like a public utility. 
     Typical cloud computing providers deliver common business applications online which are accessed from another web service or software like a web browser, while the software and data are stored remotely on servers. The cloud computing architecture uses a layered approach for providing application services. A first layer is an application layer that is executed at client computers. In this example, the application allows a client to access storage via a cloud. 
     After the application layer, is a cloud platform and cloud infrastructure, followed by a “server” layer that includes hardware and computer software designed for cloud specific services. The management system  118  (and associated methods thereof) and storage systems described above can be a part of the server layer for providing storage services. Details regarding these layers are not germane to the inventive aspects. 
     Thus, a method and apparatus for managing resources within system  100  have been described. Note that references throughout this specification to “one aspect” or “an aspect” mean that a particular feature, structure or characteristic described in connection with the aspect is included in at least one aspect of the present invention. Therefore, it is emphasized and should be appreciated that two or more references to “an aspect” or “one aspect” or “an alternative aspect” in various portions of this specification are not necessarily all referring to the same aspect. Furthermore, the particular features, structures or characteristics being referred to may be combined as suitable in one or more aspects of the present disclosure, as will be recognized by those of ordinary skill in the art. 
     While the present disclosure is described above with respect to what is currently considered its preferred aspects, it is to be understood that the disclosure is not limited to that described above. To the contrary, the disclosure is intended to cover various modifications and equivalent arrangements within the spirit and scope of the appended claims.