Patent Publication Number: US-2022239742-A1

Title: Methods and systems for managing a resource in a networked storage environment

Description:
RELATED APPLICATIONS 
     This application claims priority to and is a continuation of U.S. application Ser. No. 16/460,101, filed on Jul. 2, 2019, titled “METHODS AND SYSTEMS FOR MANAGING A RESOURCE IN A NETWORKED STORAGE ENVIRONMENT,” which claims priority to and is a continuation of U.S. Pat. No. 10,397,324, filed on Jul. 22, 2015, titled “METHODS AND SYSTEMS FOR MANAGING A RESOURCE IN A NETWORKED STORAGE ENVIRONMENT,” which are incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to managing resources in a networked storage environment. 
     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. Network storage systems are commonly used for a variety of purposes, such as providing multiple clients with access to shared data, backing up data and others. 
     A storage system typically includes at least a computing system executing a storage operating system for storing and retrieving data on behalf of one or more client computing systems (may just be referred to as “client” or “clients”). The storage operating system stores and manages shared data containers in a set of mass storage devices. 
     Quality of Service (QOS) is a metric used in a storage environment to provide certain throughput for processing input/output (I/O) requests for reading or writing data, a response time goal within, which I/O requests are processed and a number of I/O requests processed within a given time (for example, in a second (IOPS). Throughput means amount of data transferred within a given time, for example, in megabytes per second (Mb/s). 
     To process an I/O request to read and/or write data, various resources are used within a storage system, for example, processors at storage system nodes, storage devices and others. The different resources perform various functions in processing the I/O requests. As storage systems continue to expand in size, complexity and operating speeds, it is desirable to efficiently monitor and manage resource usage. Continuous efforts are being made to better manage and monitor resources of networked storage environments. 
     SUMMARY 
     In one aspect, a machine implemented method for a networked storage system is provided. The method includes receiving a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and predicting available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload. 
     In another aspect, a non-transitory, machine readable storage medium having stored thereon instructions for performing a method is provided. The storage medium includes machine executable code which when executed by at least one machine, causes the machine to: receive a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and predict available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload. 
     In yet another aspect, a system comprising a memory containing machine readable medium having machine executable code with stored instructions is provided. A processor module coupled to the memory executes the machine executable code to: receive a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and predict available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload. 
     In another aspect of the present disclosure, a machine implemented method is provided. The method includes sending a request with a resource identifier identifying a resource of a network storage environment; and receiving a response to the request including an available performance capacity of the resource, where the available performance capacity of the resource is determined by using an optimum utilization of the resource, a current utilization and a predicted utilization, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload. 
     This brief summary has been provided so that the nature of this disclosure may be understood quickly. A more complete understanding of the disclosure can be obtained by reference to the following detailed description of the various thereof in connection with the attached drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The various features of the present disclosure will now be described with reference to the drawings of the various aspects. In the drawings, the same components may 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. 1A  shows an example of a latency v. utilization curve (LvU), for determining headroom (performance capacity), according to one aspect of the present disclosure; 
         FIG. 1B  shows an example of an operating environment for the various aspects disclosed herein; 
         FIG. 2A  shows an example of a clustered storage system, used according to one aspect of the present disclosure; 
         FIG. 2B  shows an example of a performance manager, according to one aspect of the present disclosure; 
         FIG. 2C  shows an example of handling QOS requests by a storage system, according to one aspect of the present disclosure; 
         FIG. 2D  shows an example of a resource layout used by the performance manager, according to one aspect of the present disclosure; 
         FIG. 2E  shows an example of managing workloads and resources by the performance manager, according to one aspect of the present disclosure; 
         FIG. 3A  shows a format for managing various resource objects, according to one aspect of the present disclosure; 
         FIG. 3B  shows an example of certain counters that are used, according to one aspect of the present disclosure; 
         FIG. 4A  shows an example of an overall process flow for determining and analyzing headroom, according to one aspect of the present disclosure; 
         FIG. 4B  shows an example of using a custom operational point on a LvU curve, according to one aspect of the present disclosure; 
         FIG. 4C  shows an example determining an operational point, according to one aspect of the present disclosure; 
         FIG. 4D  shows a graphical illustration of sampled and actual headroom, according to one aspect of the present disclosure; 
         FIG. 4E  shows a process flow for computing headroom based on an impact of workload change, according to one aspect of the present disclosure; 
         FIG. 4F  shows a graphical illustration showing the impact of workload change on headroom, according to one aspect of the present disclosure; 
         FIG. 4G  shows a process for computing the headroom using a service level objective (SLO), according to one aspect of the present disclosure; 
         FIG. 4H  shows an example of a graphical illustration of service level headroom, computed according to one aspect of the present disclosure; 
         FIG. 4I  shows a process for computing physical headroom, according to one aspect of the present disclosure; 
         FIG. 4K  shows a graphical illustration of computed physical headroom, according to one aspect of the present disclosure; 
         FIG. 5  shows a process flow for determining an optimal point using a model based technique; according to one aspect of the present disclosure; 
         FIG. 6A  shows a process flow for determining an optimal point using observation based technique, according to one aspect of the present disclosure; 
         FIG. 6B  provides details for determining actual headroom using the observation based technique, according to one aspect of the present disclosure; 
         FIG. 7  shows an example of a storage system, used according to one aspect of the present disclosure; 
         FIG. 8  shows an example of a storage operating system, used according to one aspect of the present disclosure; and 
         FIG. 9  shows an example of a processing system, used according to one aspect of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     As a 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 general 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 hardware processor, a hardware based 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 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, at 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, in accordance with the claimed subject matter. 
     In one aspect, a performance manager module is provided that interfaces with a storage operating system to collect quality of service (QOS) data (or performance data) for various resources. QOS provides a certain throughput (i.e. amount of data that is transferred within a given time interval (for example, megabytes per seconds (MBS)), latency and/or a number of input/output operations that can be processed within a time interval, for example, in a second (referred to as IOPS). Latency means a delay in completing the processing of an I/O request and may be measured using different metrics for example, a response time in processing I/O requests. 
     Latency v Utilization Curve: In one aspect, methods and systems for managing resources in a networked storage environment is provided. The resources may be managed based on remaining (or useful) performance capacity at any given time that is available for a resource relative to a peak/optimal performance capacity without violating any performance expectations. The available performance capacity may be referred to as “headroom” that is discussed in detail below. The resource maybe any resource in the networked storage environment, including processing nodes and aggregates that are described below in detail. Peak performance capacity of a resource may be determined according to performance limits that may be set by policies (for example, QoS or service level objectives (“SLOs”) as described below). 
     In one aspect, the remaining or available performance capacity is determined from a relationship between latency and utilization of a resource.  FIG. 1A  shows an example of one such curve. Latency  133  for a given resource that is used to process workloads is shown on the vertical, Y-axis, while the utilization  131  of the resource is shown on the X-axis. 
     The latency v utilization curve shows an optimal point  137 , after which latency shows a rapid increase. Optimal point represents maximum utilization of a resource beyond which an increase in workload are associated with higher throughput gains than latency increase. Beyond the optimal point, if the workload increases at a resource, the throughput gains or utilization increase is smaller than the increase in latency. An optimal point may be determined by a plurality of techniques defined below. The optimal point may also be customized based on a service level that guarantees certain latency/utilization for a user. The use of optimal points are described below in detail. 
     An operational point  135  shows current utilization of the resource. The available performance capacity is shown as  139 . In one aspect, the operational point  135  may be determined based on current utilization of a resource. The operational point may also be determined based on the effect of internal workloads (for example, when a storage volume is moved), when a storage node is configured as a high availability failover nodes or when there are workloads that can be throttled or delayed because they may not be very critical. 
     In one aspect, headroom (or performance capacity) may be based on the following relationship: 
     
       
         
           
             Headroom 
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                   Optimal 
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                   Point 
                 
                 - 
                 
                   Operational 
                   ⁢ 
                       
                   Point 
                 
               
               
                 Optimal 
                 ⁢ 
                     
                 Point 
               
             
           
         
       
     
     Headroom may be based on current utilization and a current optimal point that is ascertained based on collected and observed data. This is referred to “sampled” headroom. The sampled headroom may be modified by updating the current utilization of the resource to reflect any high availability node pair load (defined below) or any work that can be throttled or defined as not being critical to a workload mix. The term workload mix represents user workloads at a resource. Details for computing sampled and actual headroom are provided below. 
     In one aspect, a machine implemented method is provided. The method includes receiving a resource identifier that identifying a resource of a network storage environment as an input to a processor executable application programming interface (API). The process then predicts available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload. 
     In another aspect, a machine implemented method is provided. A management application executed at a computing device may send a request a request with a resource identifier identifying a resource of a network storage environment. The application then receives a response to the request including an available performance capacity of the resource, where the available performance capacity of the resource is determined by using an optimum utilization of the resource, a current utilization and a predicted utilization, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload. 
     Before describing the various processes for generating the latency v utilization (may also be referred to as LvU), the following provides a description of the overall networked storage environment and the resources used in the operating environment for storing data. 
     System  100 :  FIG. 1B  shows an example of a system  100 , where the various adaptive aspects disclosed herein may be implemented. System  100  includes a performance manager  121  that interfaces with a storage operating system  107  of a storage system  108  for receiving QOS data. The performance manager  121  may be a processor executable module that is executed by one or more processors out of a memory device. 
     The performance manager  121  obtains the QOS data and stores it at a data structure  125 . In one aspect, performance manager  121  analyzes the QOS data for determining headroom for a given resource. Headroom related information may be stored at data structure  125 A that is described below in detail. Details regarding the various operations performed by the performance manager  121  for determining headroom are provided below. 
     In one aspect, storage system  108  has access to a set of mass storage devices  114 A- 114 N (may be referred to as storage devices  114  or simply as storage device  114 ) within at least one storage subsystem  112 . The storage devices  114  may include writable storage device media such as magnetic disks, video tape, optical, DVD, magnetic tape, non-volatile memory devices for example, solid state drives (SSDs) including self-encrypting drives, flash memory devices and any other similar media adapted to store information. The storage devices  114  may be organized as one or more groups of Redundant Array of Independent (or Inexpensive) Disks (RAID). The aspects disclosed are not limited to any particular storage device type or storage device configuration. 
     In one aspect, the storage system  108  provides a set of logical storage volumes (may be interchangeably referred to as volume or storage volume) for providing physical storage space to clients  116 A- 116 N (or virtual machines (VMs)  105 A- 105 N). A storage volume is a logical storage object and typically includes a file system in a NAS environment or a logical unit number (LUN) in a SAN environment. The various aspects described herein are not limited to any specific format in which physical storage is presented as logical storage (volume, LUNs and others) 
     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 one of the client systems, each storage volume can appear to be a single drive. However, each storage volume can represent storage space in at one storage device, an aggregate of some or all of the storage space in multiple storage devices, a RAID group, or any other suitable set of storage space. 
     A storage volume is identified by a unique identifier (Volume-ID) and is allocated certain storage space during a configuration process. When the storage volume is created, a QOS policy may be associated with the storage volume such that requests associated with the storage volume can be managed appropriately. The QOS policy may be a part of a QOS policy group (referred to as “Policy_Group”) that is used to manage QOS for several different storage volumes as a single unit. The QOS policy information may be stored at a QOS data structure  111  maintained by a QOS module  109 . QOS at the storage system level may be implemented by the QOS module  109 . QOS module  109  maintains various QOS data types that are monitored and analyzed by the performance manager  121 , as described below in detail. 
     The storage operating system  107  organizes physical storage space at storage devices  114  as one or more “aggregate”, where each aggregate is a logical grouping of physical storage identified by a unique identifier and a location. The aggregate includes a certain amount of storage space that can be expanded. Within each aggregate, one or more storage volumes are created whose size can be varied. A qtree, sub-volume unit may also be created within the storage volumes. For QOS management, each aggregate and the storage devices within the aggregates are considered as resources that are used by storage volumes. 
     The storage system  108  may be used to store and manage information at storage devices  114  based on an I/O request. The request may be based on file-based access protocols, for example, the Common Internet File System (CIFS) protocol or Network File System (NFS) protocol, over the Transmission Control Protocol/Internet Protocol (TCP/IP). Alternatively, the request 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). 
     In a typical mode of operation, a client (or a VM) transmits one or more I/O request, such as a CFS or NFS read or write request, over a connection system  110  to the storage system  108 . Storage operating system  107  receives the request, issues one or more I/O commands to storage devices  114  to read or write the data on behalf of the client system, and issues a CIFS or NFS response containing the requested data over the network  110  to the respective client system. 
     System  100  may also include a virtual machine environment where a physical resource is time-shared among a plurality of independently operating processor executable 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 or host system) which may be 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 system. 
     In one aspect, system  100  may include a plurality of computing systems  102 A- 102 N (may also be referred to individually as host platform/system  102  or simply as server  102 ) communicably coupled to the storage system  108  via the connection system  110  such as a local area network (LAN), wide area network (WAN), the Internet or any other interconnect type. As described herein, the term “communicably coupled” may refer to a direct connection, a network connection, a wireless connection or other connections to enable communication between devices. 
     Host system  102 A includes a processor executable virtual machine environment having a plurality of VMs  105 A- 105 N that may be presented to client computing devices/systems  116 A- 116 N. VMs  105 A- 105 N execute a plurality of guest OS  104 A- 104 N (may also be referred to as guest OS  104 ) that share hardware resources  120 . As described above, hardware resources  120  may include processors, memory, I/O devices, storage or any other hardware resource. 
     In one aspect, host system  102  interfaces with a virtual machine monitor (VMM)  106 , for example, a processor executed Hyper-V layer provided by Microsoft Corporation of Redmond, Wash., a hypervisor layer provided by VMWare Inc., or any other type. VMM  106  presents and manages the plurality of guest OS  104 A- 104 N executed by the host system  102 . The VMM  106  may include or interface with a virtualization layer (VIL)  123  that provides one or more virtualized hardware resource to each OS  104 A- 104 N. 
     In one aspect, VMM  106  is executed by host system  102 A with VMs  105 A- 105 N. In another aspect, VMM  106  may be executed by an independent stand-alone computing system, often referred to as a hypervisor server or VMM server and VMs  105 A- 105 N are presented at one or more computing systems. 
     It is noteworthy that different vendors provide different virtualization environments, for example, VMware Corporation, Microsoft Corporation and others. The generic virtualization environment described above with respect to  FIG. 1B  may be customized to implement the aspects of the present disclosure. Furthermore, VMM  106  (or VIL  123 ) may execute other modules, for example, a storage driver, network interface and others, the details of which are not germane to the aspects described herein and hence have not been described in detail. 
     System  100  may also include a management console  118  that executes a processor executable management application  117  for managing and configuring various elements of system  100 . Application  117  may be used to manage and configure VMs and clients as well as configure resources that are used by VMs/clients, according to one aspect. It is noteworthy that although a single management console  118  is shown in  FIG. 1B , system  100  may include other management consoles performing certain functions, for example, managing storage systems, managing network connections and other functions described below. 
     In one aspect, application  117  may be used to present storage space that is managed by storage system  108  to clients&#39;  116 A- 116 N (or VMs). The clients may be grouped into different service levels (also referred to as service level objectives or “SLOs”), where a client with a higher service level may be provided with more storage space than a client with a lower service level. A client at a higher level may also be provided with a certain QOS vis-à-vis a client at a lower level. 
     Although storage system  108  is shown as a stand-alone system, i.e. a non-cluster based system, in another aspect, storage system  108  may have a distributed architecture; for example, a cluster based system of  FIG. 2A . Before describing the various aspects of the performance manager  121 , the following provides a description of a cluster based storage system. 
     Clustered Storage System:  FIG. 2A  shows a cluster based storage environment  200  having a plurality of nodes for managing storage devices, according to one aspect. Storage environment  200  may include a plurality of client systems  204 . 1 - 204 .N (similar to clients  116 A- 116 N,  FIG. 1B ), a clustered storage system  202 , performance manager  121 , management console  118  and at least a network  206  communicably connecting the client systems  204 . 1 - 204 .N and the clustered storage system  202 . 
     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  (may be referred to as  212  and similar to storage device  114 ) that are used as resources for processing I/O requests. 
     Each of the plurality of nodes  208 . 1 - 208 . 3  is configured to include a network module (maybe referred to as N-module), a storage module (maybe referred to as D-module), and a management module (maybe referred to as M-Module), each of which can be implemented as a processor executable 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 enable the respective nodes  208 . 1 - 208 . 3  to connect to one or more of the client systems  204 . 1 - 204 .N 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 . Accordingly, each of the plurality of nodes  208 . 1 - 208 . 3  in the clustered storage server arrangement provides the functionality of a storage server. 
     The management modules  218 . 1 - 218 . 3  provide management functions for the clustered storage system  202 . The management modules  218 . 1 - 218 . 3  collect storage information regarding storage devices  212 . 
     Each node may execute or interface with a QOS module, shown as  109 . 1 - 109 . 3  that is similar to the QOS module  109 . The QOS module  109  may be executed for each node or a single QOS module may be used for the entire cluster. The aspects disclosed herein are not limited to the number of instances of QOS module  109  that may be used in a cluster. 
     A switched virtualization layer including a plurality of virtual interfaces (VIFs)  201  is provided to interface between the respective network modules  214 . 1 - 214 . 3  and the client systems  204 . 1 - 204 .N, allowing storage  212 . 1 - 212 . 3  associated with the nodes  208 . 1 - 208 . 3  to be presented to the client systems  204 . 1 - 204 .N as a single shared storage pool. 
     The clustered storage system  202  can be organized into any suitable number of virtual servers (also referred to as “vservers” or storage virtual machines (SVM)), in which each SVM represents a single storage system namespace with separate network access. Each SVM has a client domain and a security domain that are separate from the client and security domains of other SVMs. Moreover, each SVM is associated with one or more VIFs and can span one or more physical nodes, each of which can hold one or more VIFs and storage associated with one or more SVMs. Client systems can access the data on a SVM from any node of the clustered system, through the VIFs associated with that SVM. It is noteworthy that the aspects described herein are not limited to the use of SVMs. 
     Each of the nodes  208 . 1 - 208 . 3  is defined as a computing system to provide application services to one or more of the client systems  204 . 1 - 204 .N. 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 type of switching/connecting device. 
     Although  FIG. 2A  depicts an equal number (i.e., 3) of the 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. 
     Each client system  204 . 1 - 204 .N 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. 
     Performance manager  121  interfaces with the various nodes and obtains QOS data for QOS data structure  125 . Details regarding the various modules of performance manager are now described with respect to  FIG. 2B . 
     Performance Manager  121 :  FIG. 2B  shows a block diagram of system  200 A with details regarding performance manager  121  and a collection module  211 , according to one aspect. Performance manager  121  uses the concept of workloads for tracking QOS data for managing resource usage in a networked storage environment. At a high level, workloads are defined based on incoming I/O requests and use resources within storage system  202  for processing I/O requests. A workload may include a plurality of streams, where each stream includes one or more requests. A stream may include requests from one or more clients. An example, of the workload model used by performance manager  121  is shown in  FIG. 2F  and described below in detail. 
     Performance manager  121  collects a certain amount of data (for example, data for 3 hours or 30 data samples) of workload activity. After collecting the QOS data, performance manager  121  determines the headroom for a resource, as described below in detail. Performance manager  121  uses the headroom to represent available resource capacity at any given time. 
     Performance  121  includes a current headroom coordinator  221  that includes a plurality of sub-modules including a filtering module  237 , an optimal point module  225  and an analysis module  223 . The filtering module  237  filters collected QOS data (shown as incoming data  229 ) and provides the filtered data to the optimal point module  225 . The optimal point module  225  then determines an optimal point  137  for an LvU curve. In one aspect, the optimal point module  225  determines the optimal point using a plurality of techniques and the technique that provides the most reliable value (i.e. with the highest confidence level) is selected. 
     The optimal point with the LvU curve is provided to the analysis module  223  that uses the curve and determines the headroom based on one or more operational points  135 . The headroom information may be stored in a headroom data structure  125 A. Details of using the filtering module  237 , optimal point module  225  and the analysis module  223  are provided below. 
     In one aspect, the current headroom coordinator  221  and its components may be implemented as a processor executable, application programming interface (API) which provides a set of routines, protocols, and tools for building a processor executable software application that can be executed by a computing device. When the current headroom coordinator  221  is implemented as one or more APIs, then it provides software components&#39; in terms of its operations, inputs, outputs, and underlying types. The APIs may be implemented as plug-in APIs which integrate headroom computation and analysis with other management applications. 
     When the current headroom coordinator  221  is implemented as an API, then various inputs may be provided for determining headroom. For example, inputs may include a resource identifier that identifies a resource whose performance capacity is to be computed. The outputs may include headroom values, a confidence factor, and a time range for which the headroom is computed and other information. 
     In another aspect, the performance manager  121  provides a plurality of application programming interfaces (APIs)  281 , for example, a service level API  281 A that may be used to execute the machine implemented process of  FIG. 4F , a physical headroom API  281 B that is used to execute the process of  FIG. 4G  and a workload change API  281 C that executes the process of  FIG. 4E , described below in detail. The APIs receive various inputs and provide various outputs that are described below in detail. In one aspect, the headroom APIs  281 A- 281 C may be provided as stand-alone APIs or may be integrated with other products, including the current headroom coordinator  221 , storage operating system, performance manager and others. 
     The service level API  281 A receives a plurality of inputs, for example, a resource identifier and an expected service latency value. The expected service latency value is based on a SLO for a user. Other inputs may include a Boolean value to select if the resource is configured for failover or non-failover, a Boolean value to select if the headroom is sampled or actual (as described below in detail), a time range for headroom computation and a Boolean value to select if the headroom is for a peak or average value. 
     Service level API  281 A is configured to provide an output including a resource utilization value based on the expected latency value; a computed headroom relative to the expected latency value, a latency value at the optimal point, a confidence factor and a time range over which the headroom value is calculated. 
     Physical headroom API  281 B may receive a resource identifier that identifies a resource as an input. Other inputs may include a Boolean value to select if the resource is configured for failover or non-failover, a Boolean value to select if the headroom is sampled or actual (as described below in detail), a time range for headroom computation and a Boolean value to select if the headroom is for a peak or average value. 
     Physical headroom API  281 B is configured to provide an output including the computed headroom, a latency value at the optimal point, a confidence factor and a time range over which the headroom value is calculated. 
     Workload change API  281 C may receive a resource identifier that identifies a resource and a workload identifier which identifies one or more workloads that may be added or removed from the resource as inputs. Other inputs may include a starting point utilization (or throughput) to use a different operating point, additional workload capacity based on whether the workload is removed or added, a Boolean value to select if the resource is configured for failover or non-failover; a Boolean value to select if the headroom is sampled or actual (as described below in detail), a time range for headroom computation and a Boolean value to select if the headroom is for a peak or average value may also be used an input. 
     Workload change API  281 C is configured to provide an output including utilization based on a predicted operating point, latency based on the predicted operating point, computed headroom relative to the predicted operating point, a confidence factor and a time range over which the headroom value is calculated. 
     System  200 A further shows two clusters  202 A and  202 B, both similar to cluster  202  described above. Each cluster includes the QOS module  109  for implementing QOS policies and appropriate counters for collecting information regarding various resources. Cluster 1  202 A may be accessible to clients  204 . 1  and  204 . 2 , while cluster 2  202 B is accessible to clients  204 . 3 / 204 . 4 . Both clusters have access to storage subsystems  207  and storage devices  212 . 1 / 212 .N. 
     Clusters  202 A and  202 B communicate with collection module  211 . The collection module  211  may be a standalone computing device or integrated with performance manager  121 . The aspects described herein are not limited to any particular configuration of collection module  211  and performance manager  121 . 
     Collection module  211  includes one or more acquisition modules  219  for collecting QOS data from the clusters. The data is pre-processed by the pre-processing module  215  and stored as pre-processed QOS data  217  at a storage device (not shown). Pre-processing module  215  formats the collected QOS data for the performance manager  121 . Pre-processed QOS data  217  is provided to a collection module interface  231  of the performance manager  121  via the performance manager interface  213 . QOS data received from collection module  211  is stored as QOS data structure  125  (shown as incoming data  229 ) and used by the filtering module  237 , before the data is used for computing the optimal point  137 . 
     In one aspect, the performance manager  121  includes a GUI  229 . Client  205  may access headroom analysis results using GUI  229 . Before describing the various processes involving performance manager  121  and its components, the following provides an overview of QOS in general, as used by the various aspects of the present disclosure. 
     QOS Overview: As shown in  FIG. 2C , the network module  214  of a cluster includes a network interface  214 A for receiving requests from clients. Network module  214  executes a NFS module  214 C for handling NFS requests, a CIFS module  214 D for handling CIFS requests, a SCSI module  214 E for handling iSCSI requests and an others module  214 F for handling “other” requests. A node interface  214 G is used to communicate with QOS module  109 , storage module  216  and/or another network module  214 . QOS management interface  214 B is used to provide QOS data from the cluster to collection module  211  for pre-processing data. 
     QOS module  109  includes a QOS controller  109 A, a QOS request classifier  109 B and QOS policy data structure (or Policy_Group)  111 . The QOS policy data structure  111  stores policy level details for implementing QOS for clients and storage volumes. The policy determines what latency and throughput rate is permitted for a client as well as for specific storage volumes. The policy determines how I/O requests are processed for different volumes and clients. 
     The storage module  216  executes a file system  216 A (a part of storage operating system  107  described below) and includes a storage layer  216 B to interface with storage device  212 . 
     NVRAM  216 C of the storage module  216  may be used as a cache for responding to I/O requests. In one aspect, for executing a write request, the write data associated with the write request is first stored at a memory buffer of the storage module  216 . The storage module  216  acknowledges that the write request is completed after it is stored at the memory buffer. The data is then moved from the memory buffer to the NVRAM  216 C and then flushed to the storage device  212 , referred to as consistency point (CP). 
     An I/O request arrives at network module  214  from a client or from an internal process directly to file system  216 A. Internal process in this context may include a de-duplication module, a replication engine module or any other entity that needs to perform a read and/or write operation at the storage device  212 . The request is sent to the QOS request classifier  109 B to associate the request with a particular workload. The classifier  109 B evaluates a request&#39;s attributes and looks for matches within QOS policy data structure  111 . The request is assigned to a particular workload, when there is a match. If there is no match, then a default workload may be assigned. 
     Once the request is classified for a workload, then the request processing can be controlled. QOS controller  109 A determines if a rate limit (i.e. a throughput rate) for the request has been reached. If yes, then the request is queued for later processing. If not, then the request is sent to file system  216 A for further processing with a completion deadline. The completion deadline is tagged with a message for the request. 
     File system  216 A determines how queued requests should be processed based on completion deadlines. The last stage of QOS control for processing the request occurs at the physical storage device level. This could be based on latency with respect to storage device  212  or overall node capacity/utilization as described below in detail. 
     Performance Model:  FIG. 2D  shows an example of a queuing structure used by the performance manager  121  for determining headroom, according to one aspect. A user workload enters the queuing network from one end (i.e. at  233 ) and leaves at the other end. 
     Various resources are used to process I/O requests. 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, the QOS data includes a number of visits, wait time per visit and service time per visit for incident detection and analysis. For the delay center, only the number of visits and the wait time per visit at the delay center are used, as described below in detail. 
     Performance manager  121  uses different flow types for its analysis. A flow type is a logical view for modeling request processing from a particular viewpoint. 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. 
     Referring now to  FIG. 2D , delay center network  235  is a resource queue that is used to track wait time due to external networks. Storage operating system  107  often makes calls to external entities to wait on something before a request can proceed. Delay center  235  tracks this wait time using a counter (not shown). 
     Network module delay center  237  is another resource queue where I/O requests wait for protocol processing by a network module processor. This delay center  237  is used to track the utilization/capacity of the network module  216 . Overutilization of this resource may cause latency, as described below in detail. 
     NV_RAM transfer delay center  273  is used to track how the non-volatile memory may be used by cluster nodes to store write data before, the data is written to storage devices  212 , in one aspect, as described below in detail. 
     A storage aggregate (or aggregate)  239  is a resource that may include more than one storage device for reading and writing information. Aggregate  239  is tracked to determine if the aggregate is fragmented and/or over utilized, as described below in detail. 
     Storage device delay center  241  may be used to track the utilization of storage devices  212 . In one aspect, storage device utilization is based on how busy a storage device may be in responding to I/O requests. 
     In one aspect, storage module delay center  245  is used for tracking node utilization. Delay center  245  is tracked to monitor the idle time for a CPU used by the storage module  216 , the ratio of sequential and parallel operations executed by the CPU and a ratio of write duration and flushing duration for using NVRAM  216 C or an NVRAM at the storage module (not shown). 
     Nodes within a cluster communicate with each other. These may cause delays in processing I/O requests. The cluster interconnect delay center  247  is used to track the wait time for transfers using the cluster interconnect system. As an example, a single queue maybe used to track delays due to cluster interconnects. 
     There may also be delay centers due to certain internal processes of storage operating system  107  and various queues may be used to track those delays. For example, a queue may be used to track the wait for I/O requests that may be blocked for file system reasons. Another queue (Delay_Center_Susp_CP) may be used to represent the wait time for Consistency Point (CP) related to the file system  216 A. During a CP, write requests are written in bulk at storage devices and this will typically cause other write requests to be blocked so that certain buffers are cleared. 
     Workload Model:  FIG. 2E  shows an example, of the workload model used by performance manager  121 , according to one aspect. As an example, a workload may include a plurality of streams  251 A- 251 N. Each stream may have a plurality of requests  253 A- 253 N. The requests may be generated by any entity, for example, an external entity  255 , like a client system and/or an internal entity  257 , for example, a replication engine that replicates storage volumes at one or more storage location. 
     A request may have a plurality of attributes, for example, a source, a path, a destination and I/O properties. The source identifies the source from where a request originates, for example, an internal process, a host or client address, a user application and others. 
     The path defines the entry path into the storage system. For example, a path may be a logical interface (LIF) or a protocol, such as NFS, CIFS, iSCSI and Fibre Channel protocol. A destination is the target of a request, for example, storage volumes, LUNs, data containers and others. I/O properties include operation type (i.e. read/write/other), request size and any other property. 
     In one aspect, streams may be grouped together based on client needs. For example, if a group of clients make up a department on two different subnets, then two different streams with the “source” restrictions can be defined and grouped within the same workload. Furthermore, requests that fall into a workload are tracked together by performance  121  for efficiency. Any requests that don&#39;t match a user or system defined workload may be assigned to a default workload. 
     In one aspect, workload streams may be defined based on the I/O attributes. The attributes may be defined by clients. Based on the stream definition, performance manager  121  tracks workloads, as described below. 
     Referring back to  FIG. 2E , a workload uses one or more resources for processing I/O requests shown as  271 A- 271 N as part of a resource object  259 . The resources include service centers and delay centers that have been described above with respect to  FIG. 2D . For each resource, a counter/queue is maintained for tracking different statistics (or QOS data)  261 . For example, a response time  263 , and a number of visits  265 , a service time (for service centers)  267 , a wait time  269  and inter-arrival time  275  are tracked. Inter-arrival time  275  is used to track when an I/O request for reading or writing data is received at a resource. The term QOS data as used throughout this specification includes one or more of  263 ,  265 ,  267  and  269  according to one aspect. 
     Performance manager  121  may use a plurality of counter objects for resource monitoring and headroom analysis, according to one aspect. Without limiting the various adaptive aspects, an example of the various counter objects are shown and described in Table I below: 
     
       
         
           
               
               
             
               
                 TABLE I 
               
               
                   
               
               
                 Workload Object Counters 
                 Description 
               
               
                   
               
             
            
               
                 OPS 
                 A number of workload operations that are completed 
               
               
                   
                 during a measurement interval, for example, a second. 
               
               
                 Read_ops 
                 A number of workload read operations that are completed 
               
               
                   
                 during the measurement interval. 
               
               
                 Write_ops 
                 A number of workload write operations that are completed 
               
               
                   
                 during the measurement interval. 
               
               
                 Total_data 
                 Total data read and written per second by a workload. 
               
               
                 Read_data 
                 The data read per second by a workload. 
               
               
                 Write_data 
                 The data written per second by a workload. 
               
               
                 Latency 
                 The average response time for I/O requests that were 
               
               
                   
                 initiated by a workload. 
               
               
                 Read_latency 
                 The average response time for read requests that were 
               
               
                   
                 initiated by a workload. 
               
               
                 Write_latency 
                 The average response time for write requests that were 
               
               
                   
                 initiated by a workload. 
               
               
                 Classified 
                 Requests that were classified as part of a workload. 
               
               
                 Read_IO_type 
                 The percentage of reads served from various components 
               
               
                   
                 (for example, buffer cache, ext_cache or disk). 
               
               
                 Concurrency 
                 Average number of concurrent requests for a workload. 
               
               
                 Interarrival_time_sum_squares 
                 Sum of the squares of the Inter-arrival time for requests of 
               
               
                   
                 a workload. 
               
               
                   
               
            
           
         
       
     
     Without limiting the various aspects of the present disclosure, Table II below provides an example of the details associated with the object counters that are monitored by the performance manager  121 , according to one aspect: 
     
       
         
           
               
               
             
               
                 TABLE II 
               
               
                   
               
               
                 Workload Detail 
                   
               
               
                 Object Counter 
                 Description 
               
               
                   
               
             
            
               
                 Visits 
                 A number of visits to a physical resource per second; 
               
               
                   
                 this value is grouped by a service center. 
               
               
                 Service_Time 
                 A workload&#39;s average service time per visit to the 
               
               
                   
                 service center. 
               
               
                 Wait_Time 
                 A workload&#39;s average wait time per visit to the service 
               
               
                   
                 center. 
               
               
                   
               
            
           
         
       
     
     Object Hierarchy:  FIG. 3A  shows an example of a format  300  for tracking information regarding different resources that are used within a clustered storage system (for example,  202 ,  FIG. 2A ). Each resource is identified by a unique resource identifier value that is maintained by the performance manager  121 . The resource identifier value may be used to obtain available performance capacity (headroom) of a resource. 
     Format  300  maybe hierarchical in nature where various objects may have parent-child, peer and remote peer relationships, as described below. As an example, format  300  shows a cluster object  302  that may be categorized as a root object type for tracking cluster level resources. The cluster object  302  is associated with various child objects, for example, a node object  306 , QOS network object  304 , a portset object  318 , a SVM object  324  and a policy group  326 . The cluster object  302  stores information regarding the cluster, for example, the number of nodes it may have, information identifying the nodes; and any other information. 
     The QOS network object  304  is used to monitor network resources, for example, network switches and associated bandwidth used by a clustered storage system. 
     The cluster node object  306  stores information regarding a node, for example, a node identifier and other information. Each cluster node object  306  is associated with a pluralities of child objects, for example, a cache object  308 , a QOS object for a storage module  310 , a QOS object for a network module  314 , a CPU object  312  and an aggregate object  316 . The cache object  308  is used to track utilization/latency of a cache (for example, NVRAM  216 C,  FIG. 2D ). The QOS storage module  310  tracks the QOS of a storage module defined by a QOS policy data structure  111  described above in detail with respect to  FIG. 2D . The QOS network module object  314  tracks the QOS for a network module. The CPU object  312  is used to track CPU performance and utilization of a node. 
     The aggregate object  316  tracks the utilization/latency of a storage aggregate that is managed by a cluster node. The aggregate object may have various child objects, for example, a flash pool object  332  that tracks usage of a plurality of flash based storage devices (shown as “flash pool”). The flash pool object  332  may have a SSD disk object  336  that tracks the actual usage of specific SSD based storage devices. The RAID group  334  is used to track the usage of storage devices configured as RAID devices. The RAID object  334  includes a storage device object  338  (shown as a HDD (hard disk drive) that tracks the actual utilization of the storage devices. 
     Each cluster is provided a portset having a plurality of ports that may be used to access cluster resources. A port includes logic and circuitry for processing information that is used for communication between different resources of the storage system. The portset object  318  tracks the various members of the portset using a port object  320  and a LIF object  322 . The LIF object  322  includes a logical interface, for example, an IP address, while the port object  320  includes a port identifier for a port, for example, a world-wide port number (WWPN). It is noteworthy that the port object  320  is also a child object of node  306  that may use a port for network communication with clients. 
     A cluster may present one or more SVMs to client systems. The SVMs are tracked by the SVM object  324 , which is a child object of cluster  302 . Each cluster is also associated with a policy group that is tracked by a policy group object  326 . The policy group  326  is associated with SVM object  324  as well as storage volumes and LUNs. The storage volume is tracked by a volume object  328  and the LUN is tracked by a LUN object  330 . The volume object  328  includes an identifier identifying a volume, size of the volume, clients associated with the volume, volume type (i.e. flexible or fixed size) and other information. The LUN object  330  includes information that identifies the LUN (LUNID), size of the LUN, LUN type (read, write or read and write) and other information. 
       FIG. 3B  shows an example of some additional counters that are used for headroom analysis, described below in detail. These counters are related to nodes and aggregates and are in addition to the counters of Table I described above. For example, counter  306 A is used to track the utilization i.e. idle time for each node processor. Node latency counter  306 B tracks the latency at nodes based on operation types. The latency may be based on the total number of visits at a storage system node/number of operations per second for a workload. This value may not include internal or system default workloads, as described below in detail. 
     Aggregate utilization is tracked using counter  316 A that tracks the duration of how busy a device may be for processing user requests. An aggregate latency counter  316 B tracks the latency due to the storage devices within an aggregate. The latency may be based on a measured delay for each storage device in an aggregate. The use of these counters for headroom analysis is described below in detail. 
     Headroom Computation and Analysis:  FIG. 4A  shows an overall machine implemented process flow  400  for determining and analyzing headroom, according to one aspect of the present disclosure. The various process blocks may be executed by performance manager  121 . It is noteworthy that the process blocks may be executed by processor executable application programming interface (APIs) that may be made available at a management console or any computing device. 
     The process begins in block B 402 , when the storage system  108  is operational and data has been stored at the storage devices. In block B 404 , performance data (for example, latency and utilization data, inter-arrival times and/or service times) for at least the cluster nodes and aggregates has been collected. The collected data is provided to the performance manager  121 . In one aspect, current and historical QOS data may both be accessed by the performance manager  121  for headroom analysis. The performance manager  121  also obtains information regarding any events that may have occurred at the storage system level associated with the QOS data. Any policy information that is associated with the resource for which the QOS data is also obtained by the performance manager  121 . 
     In block B 406 , the filtering module  237  filters the collected data. In one aspect, potential erroneous observations such as unreasonable large latency values, variances, service times or utilizations are identified. If there is any data associated with unusual events like hardware failure or network failure that may affect performance may be discarded. For example, if a flash memory card used by a node fails and has to be replaced, then the latency for processing I/O requests with the failed card may be unreasonably high and hence data associated with that node may not be reliable for headroom computations. Any outliers in the collected and historical QOS data may also be removed (for example, the top 5-10% and the bottom 5-10% of the latency and utilization values may be discarded). 
     In one aspect, filtering module  237  may also insert missing data, according to one aspect. For example, service times for different resources are expected to be within a range based on collected historical service time data. If the collected data have a high coefficient of variation, then the collected data may not be reliable and hence may have to be corrected. 
     After the data is filtered, in block B 408 , one or more LvU curves are generated and an optimal point is determined by the optimal point module  225 . In one aspect, as an example, different techniques (for example, model based and observation based techniques) are used to generate the LvU curves and compute the optimal point. The technique that provides the most reliable optimal point is used for headroom analysis. 
     The model based technique uses current observations and queueing models to generate the LvU curve. The model based technique uses inter-arrival times and service times for a resource. The inter-arrival times track the arrival times for I/O requests at a resource, while the service times track the duration for servicing user based I/O requests. The observation based technique uses both current and historical observations of latency and utilizations for generating LvU curves. Details regarding the various optimal point techniques are provided below with respect to  FIGS. 5 and 6 . It is noteworthy that the various adaptive aspects of the present disclosure are not limited to any specific technique. 
     In block B 410 , the optimal point with the highest confidence level (i.e. the most reliable optimal point value) is selected and provided to the analysis module in block B 412 . In another aspect, the optimal point may be based on a policy based input.  FIG. 4B  shows an example of an LvU curve  428 , which uses a SLO input (for example, from a policy)  430 . The SLO input defines a latency limit that is assigned for a user/resource. The custom optimal point is determined by the intersection of the SLO input and the LvU curve, shown as  432 . 
       FIG. 4C  shows an example  434  for identifying the optimal point using the “point of diminishing returns” approach. In  FIG. 4C  the intersection of a half latency v utilization curve  436  and the overall latency curve  438  may be identified as the optimal point. 
     In block B 414 , the analysis module  223  determines the headroom (for example,  139 ,  FIG. 1A ) using the optimal point and an operational point. In one aspect, different operational points may be used for a resource based on the operating environment and how the resources are being used. For example, a current total utilization may be used as an operational point with the presumption that the current total utilization may be used to process a workload mix. As described above, a workload mix represents all user workloads utilizing one or more resources. This provides a sampled headroom for a resource. 
     In another aspect, a custom operational point may be used when a volume is identified in a policy. In another aspect, the analysis module  223  may ascertain the effect of moving workloads which may affect utilization and the operational point. In yet another aspect, the utilization of a node pair that are configured as high availability (HA) pair nodes is considered for the operational point. When nodes operate as HA pair nodes and if one of the nodes becomes unavailable, then the other node takes over workload processing. In this instance, latency/utilization of both the nodes is used for determining the operational point and computing the headroom. This headroom analysis is referred to as the actual headroom. 
       FIG. 4D  shows a graphical illustration of headroom variation for a resource, based on the analysis performed by the analysis module  223 . The Y-axis shows the utilization  431  of the resource and X-axis shows duration  433 .  FIG. 4D  shows an example of current sampled headroom as  435 A and minimum sampled headroom as  435 B. The sampled headroom is simply based on current observation values. These may be based on the model based technique, observation based technique or any other technique, as described below in detail. 
     The actual headroom is shown by the curve  437 . The minimum actual headroom is shown  445 . The minimum actual headroom is determined by evaluating internal workloads  439 , HA node workload  443  and critical workload  441 . In one aspect, as described above, the operational points for internal workloads, HA node workloads and critical workloads are determined and then used to determine the actual headroom. 
     Referring back to  FIG. 4A , in block B 416 , the plurality of headroom values are stored at data structure  125 A and may also be presented to the user. Headroom information stored at headroom data structure  125 A may include the following fields that are described in Table III below: 
     
       
         
           
               
               
             
               
                 TABLE III 
               
               
                   
               
               
                 Columns 
                 Description 
               
               
                   
               
             
            
               
                 Resource 
                 This field identifies the resource whose sampled headroom 
               
               
                 metadata 
                 is being stored at data structure 125A 
               
               
                 Index 
                 This field provides key identifiers for a workload mix e.g. 
               
               
                 counter data 
                 service time, utilization, latency 
               
               
                 Time 
                 Sample time 
               
               
                 counters 
               
               
                 Observation- 
                 Optimal Point - Based on Utilization, IOPS, Latency 
               
               
                 based 
                 Optimal Point - Confidence intervals 
               
               
                 estimates 
                 Custom Optimal Point - Based on Utilization, IOPS, latency 
               
               
                   
                 Custom Optimal Point Policy 
               
               
                   
                 Custom Optimal Point - Confidence intervals 
               
               
                   
                 Curve parameters 
               
               
                   
                 Validity 
               
               
                   
                 Indicator if picked for sampled headroom calculations 
               
               
                 Model-based 
                 Optimal Point - Based on Utilization, IOPS, Latency 
               
               
                 estimates 
                 Optimal Point - Confidence intervals 
               
               
                   
                 Custom Optimal Point - Utilization, IOPS, Latency 
               
               
                   
                 Custom Optimal Point Policy 
               
               
                   
                 Optimal Point - Confidence intervals 
               
               
                   
                 Optimal Point - Confidence intervals 
               
               
                   
                 Validity 
               
               
                   
                 Indicator if picked for sampled headroom calculations 
               
               
                 Operational 
                 Operational Point - Entire workload mix 
               
               
                 Points 
                 Operational Point - HA Node pairs 
               
               
                   
                 Operational Point - Internal throttled workloads 
               
               
                   
                 Operational Point - Custom based on SLO 
               
               
                 Headroom 
                 Sampled headroom 
               
               
                 Values 
                 Actual headroom - HA Node pairs 
               
               
                   
                 Actual headroom - (HA + Internal throttled workloads) 
               
               
                   
                 Actual headroom - Custom 
               
               
                   
               
            
           
         
       
     
     In one aspect, headroom data structure  125 A may be used for future analysis and historical comparison. 
     Headroom API Process Flows:  FIGS. 4E, 4G and 4H  show process flows executed by the headroom APIs  281  shown in  FIG. 2B . The headroom APIs access headroom and resource data for various computations and analysis and then provides performance capacity information to a user. 
     Workload Change Headroom:  FIG. 4E  shows a process  440  executed by the workload change API  281 C, according to one aspect of the present disclosure. Process  440  may be used to predict a physical headroom value for a given resource when a workload is moved or added to the resource. In one aspect, an operation point determined by using the process  400  of  FIG. 4A  is changed by the new workload. This results in a new latency/utilization value, providing a new workload headroom, Process  440  may be used for a plurality of resources, including an aggregate and a storage node. 
     The process begins in block B 442 , after the storage system has stored data. Performance data for the plurality of resources has been collected for generating the LvU curve for one or more resources. 
     In block B 444 , a resource is identified for the prediction. Each resource of system  100  is identified by a unique identifier. The resource identifier is provided to workload change API  281 C. In one aspect, the resource identifier is used as an input and received from another application executed by a computing device. Based on the identifier, the API  281 C retrieves/generates an LvU curve for the resource. 
     In block B 446 , a workload change for the resource is identified. The workload may be removed or added to the resource. Adding or removing the workload, impacts the latency and utilization of the resource and hence impacts the operating point. In block B 448 , the impact of the workload change to the operating point is ascertained. A time range for computing the headroom due to the workload change is selected in block B 450 . In one aspect, a default time range may be used. In another aspect, a custom time range may be used for headroom computation. 
     In block B 452 , a predicted headroom based on the workload change is computed. The predicted headroom may be provided to a user so that the user can better manage workloads and the resources associated with processing the workloads. The user receives the predicted headroom in response to a request that is provided to performance manager  121 . 
       FIG. 4F  shows graphical illustration of an impact of workload change. The optimal point in  FIG. 3F  is shown as  461 . A current operating point is shown as  463 . A new workload  465  is added to the resource and the resulting new operating point is shown as  467 . The new headroom based on the workload change is shown as  469 . 
     In one aspect, a machine implemented method is provided that is based on  FIG. 4E . The method includes receiving a resource identifier identifying a resource of a network storage environment as an input to the API and predicting available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload. 
     Service Level Headroom:  FIG. 4G  shows a process  456  for determining a service level headroom that is based on an optimal point determined by process  400  of  FIG. 4A  and an operation point that is based on a service level objective, in one aspect of the present disclosure. Process  456  may be used for a plurality of resources, including an aggregate and a storage node. The various process blocks may be executed by a processor executable SL API  281 A ( FIG. 2B ). 
     The process begins in block B 458 , after the storage system has stored data. Performance data for the plurality of resources has been collected for generating the LvU curve for one or more resources. In block B 460 , a resource whose service level headroom is to be determined is identified. In one aspect, each resource of system  100  is identified by a unique identifier. The resource identifier may be provided as an input to SL API  281 A. An expected latency value may also be provided as an input to the SL API  281 A. The expected latency may be based on a SLO assigned to a client. The SLO provides a guaranteed latency value for processing I/O requests. In one aspect, the resource identifier and expected latency are part of a request for obtaining service level headroom from a user or another application executed by a computing device. 
     In block B 462 , the expected latency of the resource is determined. This value may also be provided to or obtained by SL API  281 A. In one aspect, the SLO based latency value is stored at data structure  111  ( FIG. 2C ) and provided to the performance manager  121 . Based on the SLO input, an effective optimal point is ascertained in block B 464 . 
     In block B 466 , a current actual operating point is ascertained. The current operating point may be based on collected latency and utilization data, as explained above with respect to  FIG. 4A . In block B 468 , a duration (or time range) for the headroom computation is selected. In one aspect, SL API  281 A may use a default time range. In another aspect, a custom duration may be specified. 
     In one aspect, headroom using both a current operating point and the effective operating point based on the SLO input is computed in block B 470 . This provides the service level headroom that may be provided to a user or another application. In one aspect, the service level headroom is computed using the difference in resource utilization at the current operating point and the effective optimal point. 
       FIG. 4H  shows an example of service level headroom  453  determined by process  456 . An optimal point is shown as  451 , while the effective optimal point is shown as  455 . A current operating point is shown as  457 . The service level headroom  453  is based on the difference in the utilization levels/latency at  455  and  457 . The maximum service level headroom, if the utilization was zero is show as  459 . 
     In one aspect, by using the service level headroom, in block B 472 , a user can move a workload or provision a workload to a different resource, when the service level headroom reaches a certain threshold value. 
     Physical Headroom:  FIG. 4I  shows a process  474  for determining a physical headroom that is based on an optimal point determined by process  400  of  FIG. 4A , in one aspect of the present disclosure. The term physical headroom means the headroom that is based on a current operating point. The current operating point may be for a stand-alone storage system node or for a high availability node pair. 
     Process  474  may be used for a plurality of resources, including an aggregate and a storage node. The various process blocks may be executed by a processor executable physical headroom API  281 B ( FIG. 2B ). Both current and historical latency and utilization data may be used for process  474 . 
     The process begins in block B 476 , after the storage system has stored data. Performance data for the plurality of resources has been collected for generating the LvU curve for one or more resources. 
     In block B 478 , a resource whose physical headroom is to be determined is identified. In one aspect, each resource of system  100  is identified by a unique identifier. The resource identifier may be provided as an input to physical headroom API  281 B. The resource identifier may be received from a user or another application executed by a computing device. As an example, the resource may include a storage system node, an aggregate or both. 
     In block B 480 , the process determines if a storage system node, associated with the selected resource is configured as HA node with another, peer storage system node. One reason for obtaining this information is because the workloads at the peer node may affect the latency/utilization of the storage system node, in case there is a failover situation when the storage system node or the peer node have to take over each other&#39;s operation. This information may be obtained from node/cluster configuration information that is maintained by storage operating system  107  and/or performance manager  121  at a memory location (not shown). 
     In block B 482 , a time range is selected for determining the physical headroom. In block B 484 , the headroom is computed for the selected time range. When the nodes are configured as HA pair nodes, then the workloads of both the nodes may be used as an operational point. Once the headroom is determined, it can be stored and/or presented to the user in block B 486 . 
       FIG. 4K  shows an example of the physical headroom  473  that is based on the optimal point  471  and the operation point  475 . The operation point  475  is based on the current utilization for one node or for both nodes, when the nodes are configured as HA pair nodes. 
     In one aspect of the present disclosure, a machine implemented method based on the process of  FIG. 4I  is provided. The method includes sending a request with a resource identifier identifying a resource of a network storage environment. The request is sent by another application (for example,  117 , FIG.  1 B) and receiving a response to the request including an available performance capacity of the resource, where the available performance capacity of the resource is determined by using an optimum utilization of the resource, a current utilization and a predicted utilization, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload. 
     Model Based Optimal Point Determination:  FIG. 5  shows a process  500  for generating LvU curves and determining an optimal point using a model-based technique for block B 408  of  FIG. 4A , according to one aspect. The model based technique begins in block B 502 . The model based technique uses analytic models with inter-arrival times at the resources (average and variance) as well as the service times (average and variance) for each resource to process a workload mix. A queueing model is used for evaluating each resource in the networked storage environment. As an example, each resource may be modelled as a GI/G/I queue such that that a service center where requests arrive according to a general independent stochastic process and are served according to a general stochastic process by a single node, according to FCFC (First Come, First Served) methodology. If the service center includes N node resources, then the queueing model is GI/G/n. In one aspect, I/O request arrivals and service at a resources are parameterized at any given time. In one aspect, as described below, different queuing models are used for different resources. 
     In block B 504 , the optimal point module  225  uses the GI/G/N (where N is the number of cores that act as servers in the queuing model) model for N node resources, for example, a multi-core CPU of a node. 
     In block B 506 , SSD and hard drive aggregates are queued under GI/G/1 model by the optimal point module  225  because in an aggregate, the I/O requests are expected to be uniformly served by all the storage devices in the aggregate and hence GI/G/1 is an accurate representation. 
     When an aggregate is a hybrid aggregate i.e. includes both SSD and hard drives, then a GI/G/1 queueing model under the shortest job first (SJF) scheduling policy is used in block B 508  by the optimal point module  225 . The reason for using this model is because in hybrid aggregates, the service time may be variable since some I/O requests are served at a faster rate while others at a lower rate. 
     In block B 510 , the estimated latency is determined for each resource using the inter-arrival time and service time. The latency may be expressed as T r . In one aspect, Kingman&#39;s formula for GI/G/1 queues maybe used to estimate T r  of the resource based on the equation provided below: 
     
       
         
           
             
               
                 
                   
                     
                       T 
                       r 
                     
                     = 
                     
                       
                         T 
                         s 
                       
                       + 
                       
                         
                           ρ 
                           ⁢ 
                           
                             
                               T 
                               s 
                             
                             ( 
                             
                               
                                 c 
                                 a 
                                 2 
                               
                               + 
                               
                                 c 
                                 s 
                                 2 
                               
                             
                             ) 
                           
                         
                         
                           2 
                           ⁢ 
                           
                             ( 
                             
                               1 
                               - 
                               ρ 
                             
                             ) 
                           
                         
                       
                     
                   
                   , 
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
         
         
           
             T s  is the expected service time at the resource 
             p is the current utilization in the resource 
             c a   Z  and c s   Z  are the squared coefficient of variations (CV) for inter-arrival times and service times at the resource, respectively.
 
Kingman&#39;s formula is an approximation for the GI/G/1 queues. And hence a correction factor G KLB  may be used for correcting the expected latency defined by equation [2] below:
 
           
         
       
    
     
       
         
           
             
               
                 
                   
                     G 
                     KLB 
                   
                   = 
                   
                     { 
                     
                       
                         
                           
                             
                               exp 
                               ⁡ 
                               ( 
                               
                                 
                                   2 
                                   3 
                                 
                                 · 
                                 
                                   
                                     1 
                                     - 
                                     ρ 
                                   
                                   
                                     P 
                                     n 
                                   
                                 
                                 · 
                                 
                                   
                                     
                                       ( 
                                       
                                         1 
                                         - 
                                         
                                           c 
                                           a 
                                           2 
                                         
                                       
                                       ) 
                                     
                                     2 
                                   
                                   
                                     
                                       c 
                                       a 
                                       2 
                                     
                                     + 
                                     
                                       c 
                                       s 
                                       2 
                                     
                                   
                                 
                               
                               ) 
                             
                             , 
                           
                         
                         
                           
                             0 
                             ≤ 
                             
                               c 
                               a 
                               2 
                             
                             ≤ 
                             1 
                           
                         
                       
                       
                         
                           
                             
                               exp 
                               ⁡ 
                               ( 
                               
                                 
                                   - 
                                   
                                     ( 
                                     
                                       1 
                                       - 
                                       ρ 
                                     
                                     ) 
                                   
                                 
                                 ⁢ 
                                 
                                   
                                     
                                       c 
                                       a 
                                       2 
                                     
                                     - 
                                     1 
                                   
                                   
                                     
                                       c 
                                       a 
                                       2 
                                     
                                     + 
                                     
                                       c 
                                       s 
                                       2 
                                     
                                   
                                 
                               
                               ) 
                             
                             , 
                           
                         
                         
                           
                             
                               c 
                               a 
                               2 
                             
                             &gt; 
                             1 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
     The incorporation of G KLB  modifies the Kingman formula as: 
     
       
         
           
             
               
                 
                   
                     
                       T 
                       r 
                     
                     = 
                     
                       
                         T 
                         s 
                       
                       + 
                       
                         
                           
                             ρ 
                             ⁢ 
                             
                               
                                 T 
                                 s 
                               
                               ( 
                               
                                 
                                   c 
                                   a 
                                   2 
                                 
                                 + 
                                 
                                   c 
                                   s 
                                   2 
                                 
                               
                               ) 
                             
                           
                           
                             2 
                             ⁢ 
                             
                               ( 
                               
                                 1 
                                 - 
                                 ρ 
                               
                               ) 
                             
                           
                         
                         ⁢ 
                         
                           G 
                           KLB 
                         
                       
                     
                   
                   , 
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
     Which tend to return lower latency values compared to (1). 
     Latency in a GI/G/1/SJF (Shortest Job First) queue the latency is determined by: 
     
       
         
           
             
               
                 T 
                 r 
               
               ( 
               x 
               ) 
             
             = 
             
               
                 T 
                 s 
               
               + 
               
                 
                   
                     ρ 
                     ⁢ 
                     
                       ( 
                       x 
                       ) 
                     
                     ⁢ 
                     
                       
                         T 
                         s 
                       
                       ( 
                       
                         
                           c 
                           a 
                           2 
                         
                         + 
                         
                           c 
                           s 
                           2 
                         
                       
                       ) 
                     
                   
                   
                     2 
                     ⁢ 
                     
                       ( 
                       
                         1 
                         - 
                         
                           ρ 
                           ⁡ 
                           ( 
                           x 
                           ) 
                         
                       
                       ) 
                     
                   
                 
                 ⁢ 
                 
                   G 
                   KLB 
                 
               
             
           
         
       
     
     Where x is a specific service time in the full range [s min ,s max ] of the service times at the resource and 
       p(x)=pT s ∫ B     min     x tf(t)dt
 
     In the case of multiple servers the latency formula (3) described above is modified as 
     
       
         
           
             
               
                 T 
                 r 
               
               = 
               
                 
                   T 
                   s 
                 
                 + 
                 
                   
                     
                       
                         P 
                         n 
                       
                       ⁢ 
                       
                         
                           T 
                           s 
                         
                         ( 
                         
                           
                             c 
                             a 
                             2 
                           
                           + 
                           
                             c 
                             s 
                             2 
                           
                         
                         ) 
                       
                     
                     
                       2 
                       ⁢ 
                       
                         n 
                         ⁡ 
                         ( 
                         
                           1 
                           - 
                           ρ 
                         
                         ) 
                       
                     
                   
                   ⁢ 
                   
                     G 
                     KLB 
                   
                 
               
             
             , 
           
         
       
     
     Where n is the number of servers and 
     
       
         
           
             
               P 
               n 
             
             = 
             
               
                 
                   
                     ( 
                     
                       n 
                       ⁢ 
                       ρ 
                     
                     ) 
                   
                   n 
                 
                 
                   
                     n 
                       
                     ! 
                   
                   ⁢ 
                   
                     ( 
                     
                       1 
                       - 
                       ρ 
                     
                     ) 
                   
                 
               
               · 
               
                 
                   
                     [ 
                     
                       
                         
                           ∑ 
                           
                             k 
                             = 
                             0 
                           
                           
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                             - 
                             1 
                           
                         
                           
                         
                           
                             
                               ( 
                               
                                 m 
                                 ⁢ 
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                             k 
                           
                           
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                             ! 
                           
                         
                       
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                             ( 
                             
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                               ⁢ 
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                           n 
                         
                         
                           
                             n 
                               
                             ! 
                           
                           ⁢ 
                           
                             ( 
                             
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                             ) 
                           
                         
                       
                     
                     ] 
                   
                   
                     - 
                     1 
                   
                 
                 . 
               
             
           
         
       
     
     It is noteworthy that P n  is an estimation of the effective utilization in the system with n servers. The queuing system is not considered busy until all servers are busy. This is captured by expressing business as a function of the number of servers being utilized (one component in P n  for each possible busy servers). 
     At each node resource there may be three traffic types: high priority, low priority and CP operations. The accuracy of the models depends how these three traffic types are interleaved to generate the final queuing model. If we assume the traffic at the storage module is managed according to priority levels of these types, the latency of high priority traffic, may be determined by [1]: 
     
       
         
           
             
               T 
               
                 r 
                 , 
                 1 
               
             
             = 
             
               
                 T 
                 
                   s 
                   , 
                   1 
                 
               
               + 
               
                 
                   
                     
                       P 
                       n 
                     
                       
                   
                   
                     2 
                     ⁢ 
                     
                       n 
                       ⁡ 
                       ( 
                       
                         1 
                         - 
                         ρ 
                       
                       ) 
                     
                     ⁢ 
                     ρ 
                   
                 
                 ⁢ 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     N 
                   
                   
                     
                       ρ 
                       i 
                     
                     ⁢ 
                     
                       
                         T 
                         
                           s 
                           , 
                           i 
                         
                       
                       ( 
                       
                         
                           c 
                           
                             a 
                             , 
                             i 
                           
                           2 
                         
                         + 
                         
                           c 
                           
                             s 
                             , 
                             i 
                           
                           2 
                         
                       
                       ) 
                     
                     ⁢ 
                     
                       
                         G 
                         KLB 
                       
                       . 
                     
                   
                 
               
             
           
         
       
     
     Here the summation is over N levels of priorities and the subscript i refers to parameters of that type of traffic. Note that i=1 refers to high priority traffic. 
     In another aspect, all types of traffic maybe combined into a single stream without any batching or priority assumptions. In that case the resulting variance when three types of traffic is mixed is given by: 
     
       
         
           
             
               
                 σ 
                 mix 
                 2 
               
               = 
               
                 
                   
                     
                       n 
                       1 
                     
                     
                       
                         n 
                         1 
                       
                       + 
                       
                         n 
                         2 
                       
                       + 
                       
                         n 
                         3 
                       
                     
                   
                   ⁢ 
                   
                     ( 
                     
                       
                         σ 
                         1 
                         2 
                       
                       + 
                       
                         
                           ( 
                           
                             
                               μ 
                               mix 
                             
                             - 
                             
                               μ 
                               1 
                             
                           
                           ) 
                         
                         2 
                       
                     
                     ) 
                   
                 
                 + 
                 
                   
                     
                       n 
                       2 
                     
                     
                       
                         n 
                         1 
                       
                       + 
                       
                         n 
                         2 
                       
                       + 
                       
                         n 
                         3 
                       
                     
                   
                   ⁢ 
                   
                     ( 
                     
                       
                         σ 
                         2 
                         2 
                       
                       + 
                       
                         
                           ( 
                           
                             
                               μ 
                               mix 
                             
                             - 
                             
                               μ 
                               2 
                             
                           
                           ) 
                         
                         2 
                       
                     
                     ) 
                   
                   ⁢ 
                   
                     
                       n 
                       3 
                     
                     
                       
                         n 
                         1 
                       
                       + 
                       
                         n 
                         2 
                       
                       + 
                       
                         n 
                         3 
                       
                     
                   
                   ⁢ 
                   
                     ( 
                     
                       
                         σ 
                         3 
                         2 
                       
                       + 
                       
                         
                           ( 
                           
                             
                               μ 
                               mix 
                             
                             - 
                             
                               μ 
                               3 
                             
                           
                           ) 
                         
                         2 
                       
                     
                     ) 
                   
                 
               
             
             , 
           
         
       
     
     Where and n i  and μ 1  are the sample size and the mean of each traffic type and 
     
       
         
           
             
               μ 
               mix 
             
             = 
             
               
                 
                   
                     n 
                     1 
                   
                   
                     
                       n 
                       1 
                     
                     + 
                     
                       n 
                       2 
                     
                     + 
                     
                       n 
                       3 
                     
                   
                 
                 ⁢ 
                 
                   μ 
                   1 
                 
               
               + 
               
                 
                   
                     n 
                     2 
                   
                   
                     
                       n 
                       1 
                     
                     + 
                     
                       n 
                       2 
                     
                     + 
                     
                       n 
                       3 
                     
                   
                 
                 ⁢ 
                 
                   μ 
                   2 
                 
               
               + 
               
                 
                   
                     n 
                     3 
                   
                   
                     
                       n 
                       1 
                     
                     + 
                     
                       n 
                       2 
                     
                     + 
                     
                       n 
                       3 
                     
                   
                 
                 ⁢ 
                 
                   
                     μ 
                     3 
                   
                   . 
                 
               
             
           
         
       
     
     Once the variance and the mean of the inter-arrival times (or service times) the coefficient of variation associated with each process is computed and used within the GI/G/1 GI/G/n queuing formulas described above. If the variances are large and undesirable, the sample sizes n 2  and n 3 , which correspond to low priority and CP traffic may be reduced by: 
     
       
         
           
             
               n 
               i 
             
             = 
             
               
                 n 
                 i 
               
               ⁢ 
               
                 
                   n 
                   i 
                 
                 
                   
                     n 
                     1 
                   
                   + 
                   
                     n 
                     2 
                   
                   + 
                   
                     n 
                     3 
                   
                 
               
             
           
         
       
     
     Once the latency is determined by the optimal point module  225  using the foregoing models, in block B 512 , the analysis model  223  generates the LvU curves and determines the optimal point and the confidence factor associated with the optimal point. In one aspect, the confidence factor may be 10-15% of the determined optimal point. 
     The optimal point may also be determined based on policy settings such as SLO limits ( FIG. 4B ) or by identifying the point of diminishing returns in the LvU curve ( FIG. 4C ) such that increase in utilization is smaller than increase in latency. 
     In one aspect, the model based technique described above with respect to  FIG. 5  has various advantages. The model based technique avoids the need for computational intensive curve extrapolation techniques, or complex methodologies. The model based technique provides a fast and efficient way to estimate headroom. 
     Observation Based Optimal Point Determination:  FIG. 6A  shows a process  600  for generating LvU curves using the observation based technique, according to one aspect of the present disclosure. Process  600  may also be part of block B 408  of  FIG. 4A  described above. In one aspect, observation based LvU curves are based on measured observation of latency and utilization of a resource. The process recognizes that since storage operating system  107  operations can be complex, it is desirable to observe, record and categorize the relationship between latency and utilization. 
     Because performance capabilities of a resource are identified via observations, it is desirable to identify the proper resources and processes. In block B 604 , the proper resource is identified. For example, a storage and network node may be identified as the resource for monitoring. Various counters may be used to track the performance of each node, as described above with respect to  FIG. 3B . Aggregates with storage devices may also be identified for monitoring. In one aspect, nodes and aggregates are used by both user and storage system tasks to service I/O requests. 
     In B 606 , latency and utilization data is collected by the performance manager  121  for the resources identified in block B 604 . In one aspect, latency and utilization data is collected for each monitored node and aggregate. As described above, counter data for counters  306 A,  306 B,  316 A and  316 B are collected. Counter  306 A data is tracked by each node. In one aspect, counter  306 A may track the time a processor node is idle, which indicates how busy the processor may have been over a given duration. Latency counter  306 B collects latency data for both the storage and network modules. In one aspect, the latency may be based on a total number of visits at each node/number of operations per second processed by each node. This value may not include internal or system default workloads. 
     Aggregate utilization is tracked using counter  316 A that tracks the duration of how busy a device may be for processing user requests. The aggregate latency counter  316 B tracks the latency of the storage devices within an aggregate. The latency tracks the delay at each storage device. In one aspect, latency at hard drives is higher that the latency at solid state storage devices. 
     In block B 608 , the collected data for the workload is pre-processed and filtered by the optimal point module  225  using a workload mix signature. The received data is pre-processed for enhancing the accuracy and smoothness of the LvU curve. 
     An LvU curve captures the trend of how a resource sustains the demand of a workload mix. If the workload mix changes over time, then the resulting curve may be distorted. In one aspect, service time of a current workload may be used to search for stored historical latency and utilization data. The historical data for the same service time is used to augment collected data in block B 608 . It is noteworthy that other parameters, for example, read/write ratio and others may be used to filter the data. 
     In yet another aspect, collected data may be filtered based on time using the assumption that in the short-term the workload mix will stay the same. This means that the observations in the immediate past are more likely to have a similar workload mix and can be used to generate a curve. In one aspect, for different measured latencies, the optimal point module  225  estimates a (utilization, latency) value by removing observations that may be at a higher and lower end. For different latencies measured for the same utilization, mean estimators may be used to reduce the impact of outliers. 
     In block B 610 , when there are missing values in a range of collected data, then the missing values are interpolated between two observed utilizations by the optimal point module  225 . One way to interpolate the data is by using historical data for similar workload mix. 
     In block B 612 , the optimal point module  225  extrapolates incomplete LvU curve. The curve is extrapolated when after removing outlier values and using historical data to interpolate missing values, the process still generates an incomplete curve. In such an instance, the incomplete curve may be extrapolated. Different techniques may be used to extrapolate the latency v. utilization curves. For example, linear extrapolation, Newton-Gauss geometric parametric fit and other techniques. 
     In block B 614 , the optimal points as described above with respect to  FIG. 4A  are determined by the optimal point module  225 . A confidence factor for each calculated optimal point is computed. The confidence factor may be based on the quality of the curve generated from observations from a single workload mix; range of observed utilizations in the available data and the distance between the largest utilization value and the optimal point utilization value. The confidence factor may be computed by determining a mean distance of the observations from the fitted curve; the range of utilizations, such that the smaller the range higher the confidence factor or bound; and farther the optimal point from the maximum utilization, the wider the confidence bound. The confidence bounds are a prediction strength that quantifies the confidence in the estimated value. Prediction strength is an inverse of the width of the confidence bounds. 
       FIG. 6B  shows an example of a process  616  for using the observation technique results for generating the actual headroom, according to one aspect of the present disclosure. 
     The process begins in block B 620 . In block B 622 , the analysis module  223  identifies the workload or workload set that need to be considered for an internal workload; that can be throttled (or delayed) or are for an HA pair (jointly referred to as workload signature). This information again is obtained from the various counters that are maintained by the storage operating system  107 . The service time for the workload mix is computed and maybe referred to as workload mix signature. 
     In block B 624 , historical service times for the monitored resources are searched to determine if the workload mix signature is within a certain percentage (X %), for example, within 10%. This is performed by the analysis module  223 . 
     In block B 626 A, if the service time of the workload mix is within a certain percentage (X %), for example, 10% of the service time of the resource for which latency/utilization data has been collected, then the operational point may be modified by adding or reducing the utilization value of the resource. 
     If the service time is beyond X %, then a new optimal point maybe calculated based on a modified workload mix in block B 626 B. The modified workload mix (i.e. a new actual workload mix) is based on the service time of the workload mix from block B 622  with portions of the workload that is added or removed. Historical service time values are again searched for observations to modify the workload mix. The actual headroom is the difference between the new actual optimal point and the new operational point, as shown in  FIG. 4D  and described above. 
     In one aspect, the analysis module  223  validates the operational point values and their significance. For example, the analysis module  223  validates the operational point based on neighboring values, removes outliers, and marks any events that may affect the validity of the operational points. 
     In one aspect, analysis module  223  looks at “back to back” consistency points for adjusting workload mix. Typically, CP operations are conducted in the background and are given lower priority, but if the CP becomes a high priority, then the optimal point is calculated by looking at the CP traffic. 
     In another aspect, analysis module  223  evaluates single threaded behavior where the workloads access very few volumes. As a result, the LvU curves are distorted because high latencies may be observed across multiple node processors. In such a case, headroom values may be invalidated. 
     In one aspect, the observation based technique is based on selection of observations, interpolation between the observations and extrapolation beyond what is observed for a resource. The observation based techniques has various advantages, for example, using historical data with current data provides a smooth LvU curve. The optimal points using workload signature mirrors real operating environments and provides an effective headroom value. 
     Seed Curves: In one aspect, the LvU curve may be a pre-measured curve, called a seed curve that is constructed in a laboratory environment. Seed curves may be stored at a data structure by the performance manager  121 . Seed curves may be used when there are not enough observations to generate a curve. In one aspect, workload characteristics and the resources are matched with the resources and the workloads that were used to generate the seed curve. The prediction strength of the seed curve would depend on how well the resources/workloads match the workloads and resources used in the laboratory setting. 
     In one aspect, the foregoing systems and techniques provide a mechanism to determine a resource&#39;s available capacity at any given time. This allows a user to optimize resource utilization and also enables the storage system provider to meet contractual SLOs. 
     In one aspect, headroom is an efficient metric to determine performance capacity of a resource. The metric can be efficiently used in systems where a plurality of resources serve complex workloads for storing data. 
     Storage System Node:  FIG. 7  is a block diagram of a node  208 . 1  that is illustratively embodied as a storage system comprising of a plurality of processors  702 A and  702 B, a memory  704 , a network adapter  710 , a cluster access adapter  712 , a storage adapter  716  and local storage  713  interconnected by a system bus  708 . Node  208 . 1  is used as a resource and may be used to provide node and storage utilization information to performance manager  121  described above in detail. 
     Processors  702 A- 702 B 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 hardware devices. Idle time for processors  702 A- 702 A is tracked by counters  306 A, described above in detail. 
     The local storage  713  comprises one or more storage devices utilized by the node to locally store configuration information for example, in a configuration data structure  714 . The configuration information may include information regarding storage volumes and the QOS associated with each storage volume. 
     The cluster access adapter  712  comprises a plurality of ports adapted to couple node  208 . 1  to other nodes of cluster  202 . 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. In alternate aspects where the network modules and storage modules are implemented on separate storage systems or computers, the cluster access adapter  712  is utilized by the network/storage module for communicating with other network/storage-modules in the cluster  202 . 
     Each node  208 . 1  is illustratively embodied as a dual processor storage system executing a storage operating system  706  (similar to  107 ,  FIG. 1B ) that preferably implements a high-level module, such as a file system, to logically organize the information as a hierarchical structure of named directories and files at storage  212 . 1 . However, it will be apparent to those of ordinary skill in the art that the node  208 . 1  may alternatively comprise a single or more than two processor systems. Illustratively, one processor  702 A executes the functions of the network module on the node, while the other processor  702 B executes the functions of the storage module. 
     The memory  704  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 pertaining to the disclosure described herein. 
     The storage operating system  706  portions of which is typically resident in memory and executed by the processing elements, functionally organizes the node  208 . 1  by, inter alia, invoking storage operation in support of the storage service implemented by the node. 
     In one aspect, data that needs to be written is first stored at a buffer location of memory  704 . Once the buffer is written, the storage operating system acknowledges the write request. The written data is moved to NVRAM storage and then stored persistently. 
     The network adapter  710  comprises a plurality of ports adapted to couple the node  208 . 1  to one or more clients  204 . 1 / 204 .N 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  710  thus may comprise the mechanical, electrical and signaling circuitry needed to connect the node to the network. Each client  204 . 1 / 204 .N may communicate with the node over network  206  ( FIG. 2A ) by exchanging discrete frames or packets of data according to pre-defined protocols, such as TCP/IP. 
     The storage adapter  716  cooperates with the storage operating system  706  executing on the node  208 . 1  to access information requested by the clients. 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, micro-electro mechanical and any other similar media adapted to store information, including data and parity information. However, as illustratively described herein, the information is preferably stored at storage device  212 . 1 . The storage adapter  716  comprises a plurality of ports having input/output (I/O) interface circuitry that couples to the storage devices over an I/O interconnect arrangement, such as a conventional high-performance, Fibre Channel link topology. 
     Operating System:  FIG. 8  illustrates a generic example of storage operating system  706  (or  107 ,  FIG. 1B ) executed by node  208 . 1 , according to one aspect of the present disclosure. The storage operating system  706  interfaces with the QOS module  109  and the performance manager  121  such that proper bandwidth and QOS policies are implemented at the storage volume level. The storage operating system  706  may also maintain a plurality of counters for tracking node utilization and storage device utilization information. For example, counters  306 A- 306 B and  316 A- 316 C may also be maintained by the storage operating system  706  and counter information is provided to the performance manager  121 . In another aspect, performance manager  121  maintains the counters and they are updated based on information provided by the storage operating system  706 . 
     In one example, storage operating system  706  may include several modules, or “layers” executed by one or both of network module  214  and storage module  216 . These layers include a file system manager  800  that keeps track of a directory structure (hierarchy) of the data stored in storage devices and manages read/write operation, i.e. executes read/write operation on storage in response to client  204 . 1 / 204 .N requests. 
     Storage operating system  706  may also include a protocol layer  802  and an associated network access layer  806 , to allow node  208 . 1  to communicate over a network with other systems, such as clients  204 . 1 / 204 .N. Protocol layer  802  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  806  may include one or more drivers, which implement one or more lower-level protocols to communicate over the network, such as Ethernet. Interactions between clients&#39; and mass storage devices  212 . 1 - 212 . 3  (or  114 ) are illustrated schematically as a path, which illustrates the flow of data through storage operating system  706 . 
     The storage operating system  706  may also include a storage access layer  804  and an associated storage driver layer  808  to allow storage module  216  to communicate with a storage device. The storage access layer  804  may implement a higher-level storage protocol, such as RAID (redundant array of inexpensive disks), while the storage driver layer  808  may implement a lower-level storage device access protocol, such as Fibre Channel or SCSI. The storage driver layer  808  may maintain various data structures (not shown) for storing information regarding storage volume, aggregate and various storage devices. 
     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, in the case of a node  208 . 1 , 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 disclosure 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 storage device 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. It should be noted that while this description is written in terms of a write any where file system, the teachings of the present disclosure may be utilized with any suitable file system, including a write in place file system. 
     Processing System:  FIG. 9  is a high-level block diagram showing an example of the architecture of a processing system  900  that may be used according to one aspect. The processing system  900  can represent performance manager  121 , host system  102 , management console  118 , clients  116 ,  204 , or storage system  108 . Note that certain standard and well-known components which are not germane to the present aspects are not shown in  FIG. 9 . 
     The processing system  900  includes one or more processor(s)  902  and memory  904 , coupled to a bus system  905 . The bus system  905  shown in  FIG. 9  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  905 , 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 processor(s)  902  are the central processing units (CPUs) of the processing system  900  and, thus, control its overall operation. In certain aspects, the processors  902  accomplish this by executing software stored in memory  904 . A processor  902  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  904  represents any form of random access memory (RAM), read-only memory (ROM), flash memory, or the like, or a combination of such devices. Memory  904  includes the main memory of the processing system  900 . Instructions  906  implement the process steps of  FIGS. 4A, 5 and 6  described above may reside in and executed by processors  902  from memory  904 . 
     Also connected to the processors  902  through the bus system  905  are one or more internal mass storage devices  910 , and a network adapter  912 . Internal mass storage devices  910  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  912  provides the processing system  900  with the ability to communicate with remote devices (e.g., storage servers) over a network and may be, for example, an Ethernet adapter, a Fibre Channel adapter, or the like. 
     The processing system  900  also includes one or more input/output (I/O) devices  908  coupled to the bus system  905 . The I/O devices  908  may include, for example, a display device, a keyboard, a mouse, etc. 
     Cloud Computing: The system and techniques described above are applicable and especially useful in the cloud computing environment where storage is presented and shared across different platforms. 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 a network, for example, 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 storage systems/performance manager 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, methods and apparatus for managing resources in a storage environment 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 disclosure. 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 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.