Patent Publication Number: US-2023161488-A1

Title: Storage cluster load balancing based on predicted performance metrics

Description:
RELATED APPLICATION 
     The present application claims priority to Chinese Patent Application No. 202111417133.5, filed on Nov. 25, 2021 and entitled “Storage Cluster Load Balancing Based on Predicted Performance Metrics,” which is incorporated by reference herein in its entirety. 
     FIELD 
     The field relates generally to information processing, and more particularly to storage in information processing systems. 
     BACKGROUND 
     Storage arrays and other types of storage systems are often shared by multiple host devices over a network. Applications running on the host devices each include one or more processes that perform the application functionality. Such processes issue input-output (IO) operation requests for delivery to the storage systems. Storage controllers of the storage systems service such requests for TO operations. In some information processing systems, multiple storage systems may be used to form a storage cluster. 
     SUMMARY 
     Illustrative embodiments of the present disclosure provide techniques for storage cluster load balancing based on predicted performance metrics. 
     In one embodiment, an apparatus comprises at least one processing device comprising a processor coupled to a memory. The at least one processing device is configured to perform the steps of initiating load balancing for a storage cluster comprising two or more storage nodes, predicting performance metrics for the two or more storage nodes of the storage cluster at two or more time points in a designated future period of time, and selecting, based at least in part on the predicted performance metrics for the two or more storage nodes of the storage cluster at the two or more time points in the designated future period of time, a first one of the two or more storage nodes of the storage cluster as a source storage node and a second one of the two or more storage nodes of the storage cluster as a target storage node. The at least one processing device is further configured to perform the steps of determining at least one storage object residing on the source storage node which, when migrated to the target storage node, reduces a performance imbalance rate of the storage cluster for at least the designated future period of time, and performing load balancing for the storage cluster by migrating the at least one storage object from the source storage node to the target storage node. 
     These and other illustrative embodiments include, without limitation, methods, apparatus, networks, systems and processor-readable storage media. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram of an information processing system configured for storage cluster load balancing based on predicted performance metrics in an illustrative embodiment. 
         FIG.  2    is a flow diagram of an exemplary process for storage cluster load balancing based on predicted performance metrics in an illustrative embodiment. 
         FIG.  3    shows storage objects stored on storage nodes of a storage cluster in an illustrative embodiment. 
         FIG.  4    shows a plot of storage node processing loads before and after a storage object re-balancing operation in an illustrative embodiment. 
         FIG.  5    shows a plot of increasing and decreasing storage access data pattern trends in an illustrative embodiment. 
         FIG.  6    shows a plot of a cyclic storage access data pattern trend in an illustrative embodiment. 
         FIG.  7    shows a plot of an irregular storage access data pattern trend in an illustrative embodiment. 
         FIG.  8    shows a process flow for optimizing storage cluster performance through data movement across storage nodes in the storage cluster in an illustrative embodiment. 
         FIG.  9    shows a plot of storage node processing loads before and after smart storage object re-balancing operations in an illustrative embodiment. 
         FIGS.  10  and  11    show examples of processing platforms that may be utilized to implement at least a portion of an information processing system in illustrative embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Illustrative embodiments will be described herein with reference to exemplary information processing systems and associated computers, servers, storage devices and other processing devices. It is to be appreciated, however, that embodiments are not restricted to use with the particular illustrative system and device configurations shown. Accordingly, the term “information processing system” as used herein is intended to be broadly construed, so as to encompass, for example, processing systems comprising cloud computing and storage systems, as well as other types of processing systems comprising various combinations of physical and virtual processing resources. An information processing system may therefore comprise, for example, at least one data center or other type of cloud-based system that includes one or more clouds hosting tenants that access cloud resources. 
       FIG.  1    shows an information processing system  100  configured in accordance with an illustrative embodiment to provide functionality for storage cluster load balancing based on predicted performance metrics. The information processing system  100  comprises one or more host devices  102 - 1 ,  102 - 2 , . . .  102 -N (collectively, host devices  102 ) that communicate over a network  104  with one or more storage arrays  106 - 1 ,  106 - 2 , . . .  106 -M (collectively, storage arrays  106 ). The network  104  may comprise a storage area network (SAN). 
     The storage array  106 - 1 , as shown in  FIG.  1   , comprises a plurality of storage devices  108  each storing data utilized by one or more applications running on the host devices  102 . The storage devices  108  are illustratively arranged in one or more storage pools. The storage array  106 - 1  also comprises one or more storage controllers  110  that facilitate  10  processing for the storage devices  108 . The storage array  106 - 1  and its associated storage devices  108  are an example of what is more generally referred to herein as a “storage system.” This storage system in the present embodiment is shared by the host devices  102 , and is therefore also referred to herein as a “shared storage system.” In embodiments where there is only a single host device  102 , the host device  102  may be configured to have exclusive use of the storage system. 
     The host devices  102  illustratively comprise respective computers, servers or other types of processing devices capable of communicating with the storage arrays  106  via the network  104 . For example, at least a subset of the host devices  102  may be implemented as respective virtual machines of a compute services platform or other type of processing platform. The host devices  102  in such an arrangement illustratively provide compute services such as execution of one or more applications on behalf of each of one or more users associated with respective ones of the host devices  102 . 
     The term “user” herein is intended to be broadly construed so as to encompass numerous arrangements of human, hardware, software or firmware entities, as well as combinations of such entities. 
     Compute and/or storage services may be provided for users under a Platform-as-a-Service (PaaS) model, an Infrastructure-as-a-Service (IaaS) model and/or a Function-as-a-Service (FaaS) model, although it is to be appreciated that numerous other cloud infrastructure arrangements could be used. Also, illustrative embodiments can be implemented outside of the cloud infrastructure context, as in the case of a stand-alone computing and storage system implemented within a given enterprise. 
     The storage devices  108  of the storage array  106 - 1  may implement logical units (LUNs) configured to store objects for users associated with the host devices  102 . These objects can comprise files, blocks or other types of objects. The host devices  102  interact with the storage array  106 - 1  utilizing read and write commands as well as other types of commands that are transmitted over the network  104 . Such commands in some embodiments more particularly comprise Small Computer System Interface (SCSI) commands, although other types of commands can be used in other embodiments. A given IO operation as that term is broadly used herein illustratively comprises one or more such commands. References herein to terms such as “input-output” and “IO” should be understood to refer to input and/or output. Thus, an IO operation relates to at least one of input and output. 
     Also, the term “storage device” as used herein is intended to be broadly construed, so as to encompass, for example, a logical storage device such as a LUN or other logical storage volume. A logical storage device can be defined in the storage array  106 - 1  to include different portions of one or more physical storage devices. Storage devices  108  may therefore be viewed as comprising respective LUNs or other logical storage volumes. 
     The storage devices  108  of the storage array  106 - 1  can be implemented using solid state drives (SSDs). Such SSDs are implemented using non-volatile memory (NVM) devices such as flash memory. Other types of NVM devices that can be used to implement at least a portion of the storage devices  108  include non-volatile random access memory (NVRAM), phase-change RAM (PC-RAM) and magnetic RAM (MRAM). These and various combinations of multiple different types of NVM devices or other storage devices may also be used. For example, hard disk drives (HDDs) can be used in combination with or in place of SSDs or other types of NVM devices. Accordingly, numerous other types of electronic or magnetic media can be used in implementing at least a subset of the storage devices  108 . 
     In the information processing system  100  of  FIG.  1   , the storage arrays  106  are assumed to be part of a storage cluster  105  (e.g., where the storage arrays  106  may be used to implement one or more storage nodes in a cluster storage system comprising a plurality of storage nodes interconnected by one or more networks), and the host devices  102  are assumed to submit  10  operations to be processed by the storage cluster  105 . At least one of the storage controllers of the storage arrays  106  (e.g., the storage controller  110  of storage array  106 - 1 ) is assumed to implement functionality for intelligent data movement across the storage devices  108  of the storage array  106 - 1  (e.g., between different ones of the storage devices  108  or portions thereof that provide different storage tiers in the storage cluster  105 ), and between the storage array  106 - 1  and one or more other ones of the storage arrays  106 - 2  through  106 -M. Such intelligent data movement functionality is provided via a storage node and storage object performance metric prediction module  112  (also referred to as a performance metric prediction module  112 ) and a storage object movement module  114 . 
     The intelligent data movement functionality is utilized as part of storage cluster-wide load balancing operations for the storage cluster  105 . Load balancing for the storage cluster  105  may be initiated in response to various conditions, such as a user request, determining that at least a threshold amount of time has passed since load balancing was last performed, determining that a current performance imbalance rate of the storage cluster exceeds an acceptable imbalance rate threshold, etc. Once load balancing is initiated, the performance metric prediction module  112  predicts performance metrics for the two or more storage nodes (e.g., storage arrays  106 ) of the storage cluster  105  at two or more time points in a designated future period of time. The performance metric prediction module  112  then selects, based at least in part on the predicted performance metrics for the two or more storage nodes of the storage cluster at the two or more time points in the designated future period of time, a first one of the two or more storage nodes of the storage cluster as a source storage node and a second one of the two or more storage nodes of the storage cluster as a target storage node. 
     The performance metric prediction module  112  is further configured to determine at least one storage object residing on the source storage node which, when migrated to the target storage node, reduces a performance imbalance rate of the storage cluster for at least the designated future period of time. This determination may be based at least in part on predicting performance metrics for two or more storage objects residing on the source storage node at the two or more time points in the designated future period of time. The storage object movement module  114  is configured to perform load balancing for the storage cluster  105  by migrating the at least one storage object from the source storage node to the target storage node. 
     As noted above, in some embodiments the storage arrays  106  in the  FIG.  1    embodiment are assumed to be part of the storage cluster  105 . The storage cluster  105  is assumed to provide or implement multiple distinct storage tiers of a multi-tier storage system. By way of example, a given multi-tier storage system may comprise a fast tier or performance tier implemented using flash storage devices or other types of SSDs, and a capacity tier implemented using HDDs, possibly with one or more such tiers being server based. A wide variety of other types of storage devices and multi-tier storage systems can be used in other embodiments, as will be apparent to those skilled in the art. The particular storage devices used in a given storage tier may be varied depending on the particular needs of a given embodiment, and multiple distinct storage device types may be used within a single storage tier. As indicated previously, the term “storage device” as used herein is intended to be broadly construed, and so may encompass, for example, SSDs, HDDs, flash drives, hybrid drives or other types of storage products and devices, or portions thereof, and illustratively include logical storage devices such as LUNs. 
     It should be appreciated that a multi-tier storage system may include more than two storage tiers, such as one or more “performance” tiers and one or more “capacity” tiers, where the performance tiers illustratively provide increased IO performance characteristics relative to the capacity tiers and the capacity tiers are illustratively implemented using relatively lower cost storage than the performance tiers. There may also be multiple performance tiers, each providing a different level of service or performance as desired, or multiple capacity tiers. 
     Although in the  FIG.  1    embodiment the performance metric prediction module  112  and the storage object movement module  114  are shown as being implemented internal to the storage array  106 - 1  and outside the storage controllers  110 , in other embodiments one or both of the performance metric prediction module  112  and the storage object movement module  114  may be implemented at least partially internal to the storage controllers  110  or at least partially outside the storage array  106 - 1 , such as on one of the host devices  102 , one or more other ones of the storage arrays  106 - 2  through  106 -M, on one or more servers external to the host devices  102  and the storage arrays  106  (e.g., including on a cloud computing platform or other type of information technology (IT) infrastructure), etc. Further, although not shown in  FIG.  1   , other ones of the storage arrays  106 - 2  through  106 -M may implement respective instances of the performance metric prediction module  112  and the storage object movement module  114 . 
     At least portions of the functionality of the performance metric prediction module  112  and the storage object movement module  114  may be implemented at least in part in the form of software that is stored in memory and executed by a processor. 
     The host devices  102  and storage arrays  106  in the  FIG.  1    embodiment are assumed to be implemented using at least one processing platform, with each processing platform comprising one or more processing devices each having a processor coupled to a memory. Such processing devices can illustratively include particular arrangements of compute, storage and network resources. For example, processing devices in some embodiments are implemented at least in part utilizing virtual resources such as virtual machines (VMs) or Linux containers (LXCs), or combinations of both as in an arrangement in which Docker containers or other types of LXCs are configured to run on VMs. 
     The host devices  102  and the storage arrays  106  may be implemented on respective distinct processing platforms, although numerous other arrangements are possible. For example, in some embodiments at least portions of one or more of the host devices  102  and one or more of the storage arrays  106  are implemented on the same processing platform. One or more of the storage arrays  106  can therefore be implemented at least in part within at least one processing platform that implements at least a subset of the host devices  102 . 
     The network  104  may be implemented using multiple networks of different types to interconnect storage system components. For example, the network  104  may comprise a SAN that is a portion of a global computer network such as the Internet, although other types of networks can be part of the SAN, including a wide area network (WAN), a local area network (LAN), a satellite network, a telephone or cable network, a cellular network, a wireless network such as a WiFi or WiMAX network, or various portions or combinations of these and other types of networks. The network  104  in some embodiments therefore comprises combinations of multiple different types of networks each comprising processing devices configured to communicate using Internet Protocol (IP) or other related communication protocols. 
     As a more particular example, some embodiments may utilize one or more high-speed local networks in which associated processing devices communicate with one another utilizing Peripheral Component Interconnect express (PCIe) cards of those devices, and networking protocols such as InfiniBand, Gigabit Ethernet or Fibre Channel. Numerous alternative networking arrangements are possible in a given embodiment, as will be appreciated by those skilled in the art. 
     Although in some embodiments certain commands used by the host devices  102  to communicate with the storage arrays  106  illustratively comprise SCSI commands, other types of commands and command formats can be used in other embodiments. For example, some embodiments can implement IO operations utilizing command features and functionality associated with NVM Express (NVMe), as described in the NVMe Specification, Revision 1.3, May 2017, which is incorporated by reference herein. Other storage protocols of this type that may be utilized in illustrative embodiments disclosed herein include NVMe over Fabric, also referred to as NVMeoF, and NVMe over Transmission Control Protocol (TCP), also referred to as NVMe/TCP. 
     The storage array  106 - 1  in the present embodiment is assumed to comprise a persistent memory that is implemented using a flash memory or other type of non-volatile memory of the storage array  106 - 1 . More particular examples include NAND-based flash memory or other types of non-volatile memory such as resistive RAM, phase change memory, spin torque transfer magneto-resistive RAM (STT-MRAM) and Intel Optane™ devices based on 3D XPoint™ memory. The persistent memory is further assumed to be separate from the storage devices  108  of the storage array  106 - 1 , although in other embodiments the persistent memory may be implemented as a designated portion or portions of one or more of the storage devices  108 . For example, in some embodiments the storage devices  108  may comprise flash-based storage devices, as in embodiments involving all-flash storage arrays, or may be implemented in whole or in part using other types of non-volatile memory. 
     As mentioned above, communications between the host devices  102  and the storage arrays  106  may utilize PCIe connections or other types of connections implemented over one or more networks. For example, illustrative embodiments can use interfaces such as Internet SCSI (iSCSI), Serial Attached SCSI (SAS) and Serial ATA (SATA). Numerous other interfaces and associated communication protocols can be used in other embodiments. 
     The storage arrays  106  in some embodiments may be implemented as part of a cloud-based system. 
     It should therefore be apparent that the term “storage array” as used herein is intended to be broadly construed, and may encompass multiple distinct instances of a commercially-available storage array. 
     Other types of storage products that can be used in implementing a given storage system in illustrative embodiments include software-defined storage, cloud storage, object-based storage and scale-out storage. Combinations of multiple ones of these and other storage types can also be used in implementing a given storage system in an illustrative embodiment. 
     In some embodiments, a storage system comprises first and second storage arrays arranged in an active-active configuration. For example, such an arrangement can be used to ensure that data stored in one of the storage arrays is replicated to the other one of the storage arrays utilizing a synchronous replication process. Such data replication across the multiple storage arrays can be used to facilitate failure recovery in the system  100 . One of the storage arrays may therefore operate as a production storage array relative to the other storage array which operates as a backup or recovery storage array. 
     It is to be appreciated, however, that embodiments disclosed herein are not limited to active-active configurations or any other particular storage system arrangements. Accordingly, illustrative embodiments herein can be configured using a wide variety of other arrangements, including, by way of example, active-passive arrangements, active-active Asymmetric Logical Unit Access (ALUA) arrangements, and other types of ALUA arrangements. 
     These and other storage systems can be part of what is more generally referred to herein as a processing platform comprising one or more processing devices each comprising a processor coupled to a memory. A given such processing device may correspond to one or more virtual machines or other types of virtualization infrastructure such as Docker containers or other types of LXCs. As indicated above, communications between such elements of system  100  may take place over one or more networks. 
     The term “processing platform” as used herein is intended to be broadly construed so as to encompass, by way of illustration and without limitation, multiple sets of processing devices and one or more associated storage systems that are configured to communicate over one or more networks. For example, distributed implementations of the host devices  102  are possible, in which certain ones of the host devices  102  reside in one data center in a first geographic location while other ones of the host devices  102  reside in one or more other data centers in one or more other geographic locations that are potentially remote from the first geographic location. The storage arrays  106  may be implemented at least in part in the first geographic location, the second geographic location, and one or more other geographic locations. Thus, it is possible in some implementations of the system  100  for different ones of the host devices  102  and the storage arrays  106  to reside in different data centers. 
     Numerous other distributed implementations of the host devices  102  and the storage arrays  106  are possible. Accordingly, the host devices  102  and the storage arrays  106  can also be implemented in a distributed manner across multiple data centers. 
     Additional examples of processing platforms utilized to implement portions of the system  100  in illustrative embodiments will be described in more detail below in conjunction with  FIGS.  10  and  11   . 
     It is to be understood that the particular set of elements shown in  FIG.  1    for storage cluster load balancing based on predicted performance metrics is presented by way of illustrative example only, and in other embodiments additional or alternative elements may be used. Thus, another embodiment may include additional or alternative systems, devices and other network entities, as well as different arrangements of modules and other components. 
     It is to be appreciated that these and other features of illustrative embodiments are presented by way of example only, and should not be construed as limiting in any way. 
     An exemplary process for storage cluster load balancing based on predicted performance metrics will now be described in more detail with reference to the flow diagram of  FIG.  2   . It is to be understood that this particular process is only an example, and that additional or alternative processes for storage cluster load balancing based on predicted performance metrics may be used in other embodiments. 
     In this embodiment, the process includes steps  200  through  208 . These steps are assumed to be performed by the performance metric prediction module  112  and the storage object movement module  114 . The process begins with step  200 , initiating load balancing for a storage cluster comprising two or more storage nodes. 
     In step  202 , performance metrics for the two or more storage nodes of the storage cluster at two or more time points in a designated future period of time are predicted. Step  202  may include, for a given one of the two or more storage nodes of the storage cluster, determining an access frequency trend pattern for the given storage node and utilizing the access frequency trend pattern to calculate a predicted performance metric for the given storage node at each of the two or more time points in the designated future period of time. Determining the access frequency trend pattern for the given storage node may comprise generating a trend function for predicting a total amount of data accesses for the given storage node at the two or more time points in the designated future period of time. The access frequency trend pattern for the given storage node may comprise one of an increasing access frequency trend pattern and a decreasing access frequency trend pattern, and the prediction function may be generated utilizing a least squares algorithm. The access frequency trend pattern may comprise a cyclic access frequency trend pattern, and the prediction function may be generated utilizing at least one of an autocorrelation algorithm and a discrete Fourier transform algorithm. The access frequency trend pattern may comprise an irregular access frequency pattern, and the prediction function may be generated utilizing an average of historical data accesses for the given storage node over a previous period of time. 
     The  FIG.  2    process continues with step  204 , selecting, based at least in part on the predicted performance metrics for the two or more storage nodes of the storage cluster at the two or more time points in the designated future period of time, a first one of the two or more storage nodes of the storage cluster as a source storage node and a second one of the two or more storage nodes of the storage cluster as a target storage node. The first one of the two or more storage nodes of the storage cluster selected as the source storage node has a higher sum of predicted performance metrics for the two or more time points in the designated future period of time than the second one of the two or more storage nodes of the storage cluster selected as the target storage node. 
     Step  206  includes determining at least one storage object residing on the source storage node which, when migrated to the target storage node, reduces a performance imbalance rate of the storage cluster for at least the designated future period of time. In some embodiments, the determination in step  206  is based at least in part on predicting performance metrics for two or more storage objects residing on the source storage node at the two or more time points in the designated future period of time. Predicting the performance metrics for the two or more storage objects residing on the source storage node at the two or more time points in the designated future period of time may comprise: determining access frequency trend patterns for the two or more storage objects residing on the source storage node; utilizing the determined access frequency trend patterns to calculate predicted performance metrics for the two or more storage objects residing on the source storage node at each of the two or more time points in the designated future period of time; and selecting a given one of the two or more storage objects residing on the source storage node as the at least one storage object based at least in part on a comparison of sums of the predicted performance metrics for the two or more time points in the designated future period of time for the two or more storage objects residing on the source storage node. Selecting the given one of the two or more storage objects residing on the source storage node as the at least one storage object may be further based at least in part on types of access frequency trend patterns associated with the two or more storage objects residing on the source storage node. Selecting the given one of the two or more storage objects residing on the source storage node as the at least one storage object may be further or alternatively based at least in part on a confidence in the predicted performance metrics for the two or more storage objects residing on the source storage node. 
     In step  208 , load balancing for the storage cluster is performed by migrating the at least one storage object from the source storage node to the target storage node. In some embodiments, initiating load balancing for the storage cluster in step  200  is responsive to detecting that a current performance imbalance rate of the storage cluster exceeds a first designated threshold imbalance rate. Steps  202  through  208  may be repeated until the current performance imbalance rate of the storage cluster is below a second designated threshold imbalance rate. The second designated threshold imbalance rate may be less than the first designated threshold imbalance rate. 
     Storage object load balancing is a feature that allows for optimizing storage resource utilization in storage clusters. Storage object load balancing functionality identifies over-committed storage nodes in a storage cluster, and live migrates storage objects (e.g., LUNs, filesystems, datastores, files, etc.) from the over-committed storage nodes to under-committed storage nodes in the storage cluster. Illustrative embodiments provide a novel performance balancing mechanism for storage clusters. In some embodiments, performance trends are learned from historical data for each storage node of a storage cluster, and for each storage object stored on the storage nodes of the storage cluster. Such learned performance trends for the storage nodes and storage objects are then leveraged for performing smart storage cluster-wide performance balancing operations. The smart storage cluster-wide performance balancing operations are performed by balancing predictions of storage node and storage object performance during a future time period (e.g., 10 days). Thus, performing storage cluster-wide performance balancing at a given time will keep the whole storage cluster&#39;s performance distribution balanced for a relatively long time (e.g., the future time period over which the storage node and storage object performance predictions are made). It should be noted that, the storage cluster-wide performance balancing does not necessarily provide an immediate optimal balancing for the storage cluster (e.g., due to consideration of predicted performance over some designated future time period) but balancing is improved overall for the designated future time period over which the performance predictions are made. Illustrative embodiments are thereby able to improve performance balancing across the storage cluster by keeping storage object distribution across storage nodes of the storage cluster as balanced as possible for a longer period of time than is possible with conventional approaches. A storage cluster (e.g., storage cluster  105 ) is a configuration of multiple storage nodes (e.g., storage arrays  106 ) whose resources are aggregated together as a pool of resources contributed to the storage cluster. The resources may include processing resources (e.g., CPU or other compute resources), memory resources, network resources, and storage resources. Each storage node of the storage cluster contributes a set of such resources. In a storage cluster, it is important to balance workloads across each storage node to mitigate the risk of growing workloads negatively affecting performance. 
       FIG.  3    illustrates a storage cluster  305  which includes multiple storage nodes  301 - 1 ,  301 - 2 , . . .  301 -S (collectively, storage nodes  301 ). Each of the storage nodes  301  stores a respective set of storage objects: the storage node  301 - 1  stores storage objects  310 - 1 - 1 ,  310 - 1 - 2 , . . .  310 - 1 -O (collectively, storage objects  310 - 1 ), the storage node  301 - 2  stores storage objects  310 - 2 - 1 ,  310 - 2 - 2 , . . .  310 - 2 -O (collectively, storage objects  310 - 2 ), and the storage node  301 -S stores storage objects  310 - 5 - 1 ,  310 -S- 2 , . . .  310 -S-O (collectively, storage objects  310 -S). The storage objects  310 - 1 ,  310 - 2 , . . .  310 -S are collectively referred to as storage objects  310 . It should be appreciated that the particular number “O” of storage objects on each of the storage nodes  301  may differ. For example, the value of “O” for storage node  301 - 1  may be different than the value of “O” for storage node  301 - 2 . 
     Conventional storage cluster load balancing mechanisms typically focus on balancing storage object load based on or at a single time point. Thus, such conventional storage cluster load balancing mechanisms may only achieve relatively short-term performance balancing. As storage object loads change over time, the load balancing achieved by conventional storage cluster load balancing mechanisms may be broken relatively soon after a load balancing operation is performed. Thus, load balancing operations need to be performed again and again in order to keep storage object load balanced between the storage nodes of the storage cluster. Further, frequent storage object migration operations themselves increase load on the storage nodes of the storage cluster which is not efficient. 
       FIG.  4    shows a plot  400  of storage node processing load versus time. In the  FIG.  4    example, there are six storage nodes in the storage cluster. A conventional load balancing operation is performed at a first point in time  401  (e.g., time t 1 ), where the conventional load balancing operation takes into account only current storage node performance at one time point (e.g., time t 1 ). Thus, as can be seen from the plot  400 , the load balancing operation&#39;s balancing effect is temporary and the storage node processing load is un-balanced again at a second point in time  402  (e.g., before a time t 5 ). The storage node performance distribution, in some cases, may worsen quickly following the load balancing operation performed at time t 1 . Thus, additional load balancing operations are needed frequently using such conventional approaches. 
     In illustrative embodiments, a storage cluster performance balancing mechanism is used which learns each storage node&#39;s and storage object&#39;s performance trend from historical data, and which leverages such performance trends to perform storage cluster-wide balancing through predicting storage node and storage object performance during a future time period (e.g., 10 days). Thus, each load balancing operation is expected to keep the storage cluster&#39;s performance distribution balanced for a relatively long time period, such as over at least the future time period (e.g., 10 days) in which storage node and storage object performance was predicted. In this way, embodiments can efficiently improve storage cluster performance balancing by keeping storage object distribution as balanced as possible with relatively fewer re-balancing operations (e.g., and thus, relatively fewer storage object movement operations). 
     In storage systems, most data (e.g., storage objects) exhibit access frequency patterns that vary over time. The access frequency patterns may vary according to how end-users or customers utilize the storage systems (e.g., specific customer business access frequency patterns). Non-stationary time series data is a focus since this is where valuable forecasting can take place. There are several patterns for non-stationary time series, including but not limited to increasing or decreasing trend patterns, cyclic trend patterns, and irregular trend patterns. 
     Increasing or decreasing trend patterns refer to long-term increase or decrease in access frequency.  FIG.  5    shows a plot  500  illustrating decreasing and increasing wear level access patterns. Increasing and decreasing trend patterns may be linear or non-linear (e.g., exponential). In some embodiments, increasing and decreasing trend patterns are determined by leveraging least squares methods or other regression analysis. The method of least squares is an approach used in regression analysis, with important applications in data fitting. Least squares problems generally fall into one of two categories: linear or ordinary least squares; and nonlinear least squares. 
     Cyclic trend patterns refer to access frequency which rises and falls with a certain regularity. One common example is seasonality data, whose time series is affected by seasonal factors and data is in a fixed or known regularity (e.g., daily, weekly, monthly, yearly, etc.).  FIG.  6    shows a plot  600  illustrating a cyclic wear level access pattern. To determine whether data access frequency is cyclic, autocorrelation and discrete Fourier transform methods may be leveraged to detect the periodicity and to further determine the period or frequency of a cyclic or seasonal time series. In the plot  600  of  FIG.  6   , for example, the wear level exhibits a quarterly cyclic trend pattern. 
     Irregular trend patterns refer to access frequency which changes randomly or is otherwise not predictable over some designated time frame.  FIG.  7    shows a plot  700  illustrating an irregular wear level access pattern. In some embodiments, if data does not follow an increasing trend pattern, a decreasing trend pattern or a cyclic trend pattern, the data is classified as having an irregular trend pattern. 
     It should be appreciated that in other embodiments various other access frequency trend patterns may be used, and that various other methods may be used for identifying whether data access exhibits different access frequency trend patterns. 
     When triggering a smart rebalancing operation, storage object performance first needs to be predicted in a future time period. The future time period, in some embodiments, begins at the same time or close to the time at which the smart rebalancing operation is triggered. Trend functions for storage node and storage object performance over time may be determined using various data pattern detection and statistical analysis/data fitting models (e.g., least squares methods, autocorrelation, discrete Fourier transform methods, etc.). The performance of a storage node i at time point t, and the performance of a storage object j at time point t can be calculated by trend functions as follows: 
       Pred P   Node i,t =TrendFunc(Node i,t ) 
       Pred P   Object j,t =TrendFunc(Object j,t ) 
     In the equations above, TrendFunc denotes a trend function determined using data pattern detection and statistical analysis/data fitting models such as least squares methods, autocorrelation, discrete Fourier transform methods, etc. 
     Suppose there are K periodic sampling time points for a current re-balancing operation—t 1 , t 2 , t 3 , . . . t K . The particular number K may selected by an end-user which determines a suitable and reasonable sampling period according to real-world usage scenarios. The sum of predicted performance of storage node i across the K periodic sampling points is calculated according to the following equation: 
     
       
         
           
             
               SumPredP 
               
                 Node 
                 ⁢ 
                     
                 i 
               
             
             = 
             
               
                 
                   ∑ 
                   
                     k 
                     = 
                     1 
                   
                   K 
                 
                 
                   PredP 
                   
                     
                       Node 
                       ⁢ 
                           
                       i 
                     
                     , 
                     
                       t 
                       k 
                     
                   
                 
               
               = 
               
                 
                   ∑ 
                   
                     k 
                     = 
                     1 
                   
                   K 
                 
                 
                   TrendFunc 
                   ⁡ 
                   ( 
                   
                     
                       Node 
                       ⁢ 
                           
                       i 
                     
                     , 
                     
                       t 
                       k 
                     
                   
                   ) 
                 
               
             
           
         
       
     
     The sum of predicted performance of storage object j across the K periodic sampling points is calculated according to the following equation: 
     
       
         
           
             
               SumPredP 
               
                 Object 
                 ⁢ 
                     
                 j 
               
             
             = 
             
               
                 
                   ∑ 
                   
                     k 
                     = 
                     1 
                   
                   K 
                 
                 
                   PredP 
                   
                     
                       Object 
                       ⁢ 
                           
                       j 
                     
                     , 
                     
                       t 
                       k 
                     
                   
                 
               
               = 
               
                 
                   ∑ 
                   
                     k 
                     = 
                     1 
                   
                   K 
                 
                 
                   TrendFunc 
                   ⁡ 
                   ( 
                   
                     
                       Object 
                       ⁢ 
                           
                       j 
                     
                     , 
                     
                       t 
                       k 
                     
                   
                   ) 
                 
               
             
           
         
       
     
     A set of rules is then used to rank storage nodes and storage objects, so as to determine which of them have the most potential of poor performance (e.g., high workload) in a future time period. The first step is to periodically sample each target (e.g., a storage node or storage object) by predicting its performance at certain time points in the future time period. The next step is to calculate, for each target (e.g., a storage node or storage object), its sum of predicted performance in the different sampling time points. The targets (e.g., storage nodes or storage objects) are then ranked by their respective sum of predicted performance values. 
     Based on the newly-introduced method for predicting storage node and storage object performance, a novel cluster storage object distribution rebalancing algorithm is implemented. First, a current performance imbalance rate, denoted μ, of a storage cluster is periodically evaluated according to the following equation: 
     
       
         
           
             μ 
             = 
             
               
                 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       N 
                     
                     
                       
                         ( 
                         
                           
                             P 
                             
                               Node 
                               ⁢ 
                                   
                               i 
                             
                           
                           - 
                           
                             P 
                             
                               Node 
                               ⁢ 
                                   
                               average 
                             
                           
                         
                         ) 
                       
                       2 
                     
                   
                   
                     N 
                     - 
                     1 
                   
                 
               
               
                 P 
                 
                   Node 
                   ⁢ 
                       
                   average 
                 
               
             
           
         
       
     
     In the equation above, P Node i  denotes the performance of storage node i, and N denotes the number of storage arrays or storage nodes in the storage cluster. The bigger the value of P Node i , the higher the workload of the storage node i and the poorer the performance of the storage node i is. 
     When the current imbalance rate μ exceeds an acceptable threshold value Θ, a storage object relocation algorithm is triggered.  FIG.  8    illustrates a process flow  800  for the storage object relocation algorithm, which starts in step  801 . In step  803 , the imbalance value μ of the storage cluster is calculated. In step  805 , a determination is made as to whether the current imbalance rate μ exceeds the acceptable threshold value Θ. If the result of the step  805  determination is no, the process flow  800  ends in step  817 . If the result of the step  805  determination is yes, the process flow  800  proceeds to step  807 . In step  807 , SumPredP Node i  is calculated for each storage node i in the storage cluster, with the storage node with maximum SumPredP Node i  being selected as a source storage node, and the storage node with minimum SumPredP Node i  being selected as a destination storage node. It should be appreciated that in some embodiments, step  807  may include calculating SumPredP Node i  for only a subset of the storage nodes in the storage cluster rather than all storage nodes of the storage cluster. The particular number of the storage nodes in the subset may be user-configurable or based on some other factor. As an example, SumPredP Node i  values may be calculated for different storage nodes in the storage cluster until it is determined that there is at least a threshold difference between a highest calculated SumPredP Node i  value and a lowest calculated SumPredP Node i  value, such that there would be at least a threshold benefit in moving storage objects between the storage node with the highest calculated SumPredP Node i  value and the storage node with the lowest calculated SumPredP Node i  value. 
     In step  809 , SumPredP Object j  is calculated for each storage object j residing in the source storage node, and the storage object with maximum SumPredP Object  is selected as a target storage object. It should be appreciated that in some embodiments, step  809  may include calculating SumPredP Object  for only a subset of the storage objects residing in the source storage node, rather than for all storage objects residing on the source storage node. The particular number of the storage objects in the subset may be user-configurable or based on some other factor. As an example, SumPredP Object  values may be calculated for different storage objects residing in the source storage node until it is determined that there is at least a threshold difference between a highest calculated SumPredP Object  value and a lowest calculated SumPredP Object  value, such that there would be at least a threshold benefit in moving the storage object with the highest calculated SumPredP Object  value over the storage object with the lowest calculated SumPredP Object  value. 
     The target storage object is moved from the source storage node to the destination storage node in step  811 . In step  813 , the current imbalance rate μ is re-calculated after relocation of the target storage object. A determination is made in step  815  as to whether μ≤Φ. Here, Φ is the expected performance imbalance rate of the storage cluster that an end-user intends to achieve. The value of Φ is selected to avoid excessive storage object migration operations, which can cause resource contention. If the result of the step  815  determination is yes, the process flow  800  ends in step  817 . If the result of the step  815  determination is no, the process flow returns to step  807 . 
     In the process flow  800 , storage node and storage object performance are predicted by sampling in a designated future time period, and the most “valued” storage object (e.g., with the maximum value of SumPredP Object ) residing in a higher-loaded storage node in the storage cluster (e.g., the source storage node) is moved from the higher-loaded storage node in the storage cluster to an optimal lower-loaded storage node in the storage cluster (e.g., the target storage node). 
     In some embodiments, additional characteristics or factors are taken into account when selecting the source and target storage nodes, as well as the most “valued” storage object to be moved from the source storage node to the target storage node in the storage cluster. Such characteristics and factors may include the type of access frequency trend pattern which is predicted for a given storage object. As an example, in some embodiments storage objects that are predicted to have an irregular access frequency trend pattern in the future time period may be considered poor candidates to be moved as part of cluster-wide load balancing (e.g., as such storage objects&#39; associated access frequency is difficult to predict over the future time period). 
     Such characteristics and factors may also or alternatively include confidence in the predicted performance of the source storage node, the target storage node and the storage objects. For example, if the predicted access frequency trend pattern for a given storage object is below some designated confidence threshold, this may indicate that the given storage object is a poor candidate to be moved as part of cluster-wide load balancing (e.g., as the predicted access frequency trend pattern may be incorrect due to the low confidence in the prediction). 
     Additional characteristics and factors may include user-specified rules for: certain storage objects which should not be moved between storage nodes; whether different storage objects should or should not be co-located on the same storage node in the storage cluster; etc. Various other characteristics and factors may be taken into account when selecting the source storage node, the target storage node, and the storage object(s) to be moved from the source storage node to the target storage node. 
       FIG.  9    shows a plot  900  of storage node processing load versus time, similar to the plot  400  of  FIG.  4    but where the process flow  800  is used for storage cluster-wide load balancing rather than a conventional load balancing approach. In the  FIG.  9    example, there are six storage nodes in the storage cluster. The process flow  800  is performed at a first point in time  901  (e.g., a time t 1 ), which takes into account predictions of storage node and storage object performance over multiple time points (e.g., times t 1  through t 5 ). Thus, as can be seen from the plot  900 , the balancing effect is more long-term (e.g., as compared with the conventional load balancing performed in the  FIG.  4    example) and the storage node processing load remains balanced for a longer period of time through at least time  903  (e.g., through at least time t 5 ) corresponding to at least the length of time where the storage node and storage object performance is predicted. Thus, the frequency of load balancing operations can advantageously be reduced. Re-balancing is based on comprehensive storage object and storage node performance analysis, enabling the storage cluster to achieve well-balanced performance with no need to perform re-balancing again at time t 5 . This not only improves the efficiency of re-balancing, but also reduces consumption of resources in the storage cluster (e.g., processing, memory, storage and network resources of the storage nodes in the storage cluster) and reduces service reliability issues caused by frequent storage object movement. 
     It is to be appreciated that the particular advantages described above and elsewhere herein are associated with particular illustrative embodiments and need not be present in other embodiments. Also, the particular types of information processing system features and functionality as illustrated in the drawings and described above are exemplary only, and numerous other arrangements may be used in other embodiments. 
     Illustrative embodiments of processing platforms utilized to implement functionality for storage cluster load balancing based on predicted performance metrics will now be described in greater detail with reference to  FIGS.  10  and  11   . Although described in the context of system  100 , these platforms may also be used to implement at least portions of other information processing systems in other embodiments. 
       FIG.  10    shows an example processing platform comprising cloud infrastructure  1000 . The cloud infrastructure  1000  comprises a combination of physical and virtual processing resources that may be utilized to implement at least a portion of the information processing system  100  in  FIG.  1   . The cloud infrastructure  1000  comprises multiple virtual machines (VMs) and/or container sets  1002 - 1 ,  1002 - 2 , . . .  1002 -L implemented using virtualization infrastructure  1004 . The virtualization infrastructure  1004  runs on physical infrastructure  1005 , and illustratively comprises one or more hypervisors and/or operating system level virtualization infrastructure. The operating system level virtualization infrastructure illustratively comprises kernel control groups of a Linux operating system or other type of operating system. 
     The cloud infrastructure  1000  further comprises sets of applications  1010 - 1 ,  1010 - 2 , . . .  1010 -L running on respective ones of the VMs/container sets  1002 - 1 ,  1002 - 2 , . . .  1002 -L under the control of the virtualization infrastructure  1004 . The VMs/container sets  1002  may comprise respective VMs, respective sets of one or more containers, or respective sets of one or more containers running in VMs. 
     In some implementations of the  FIG.  10    embodiment, the VMs/container sets  1002  comprise respective VMs implemented using virtualization infrastructure  1004  that comprises at least one hypervisor. A hypervisor platform may be used to implement a hypervisor within the virtualization infrastructure  1004 , where the hypervisor platform has an associated virtual infrastructure management system. The underlying physical machines may comprise one or more distributed processing platforms that include one or more storage systems. 
     In other implementations of the  FIG.  10    embodiment, the VMs/container sets  1002  comprise respective containers implemented using virtualization infrastructure  1004  that provides operating system level virtualization functionality, such as support for Docker containers running on bare metal hosts, or Docker containers running on VMs. The containers are illustratively implemented using respective kernel control groups of the operating system. 
     As is apparent from the above, one or more of the processing modules or other components of system  100  may each run on a computer, server, storage device or other processing platform element. A given such element may be viewed as an example of what is more generally referred to herein as a “processing device.” The cloud infrastructure  1000  shown in  FIG.  10    may represent at least a portion of one processing platform. Another example of such a processing platform is processing platform  1100  shown in  FIG.  11   . 
     The processing platform  1100  in this embodiment comprises a portion of system  100  and includes a plurality of processing devices, denoted  1102 - 1 ,  1102 - 2 ,  1102 - 3 , . . .  1102 -K, which communicate with one another over a network  1104 . 
     The network  1104  may comprise any type of network, including by way of example a global computer network such as the Internet, a WAN, a LAN, a satellite network, a telephone or cable network, a cellular network, a wireless network such as a WiFi or WiMAX network, or various portions or combinations of these and other types of networks. 
     The processing device  1102 - 1  in the processing platform  1100  comprises a processor  1110  coupled to a memory  1112 . 
     The processor  1110  may comprise a microprocessor, a microcontroller, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a central processing unit (CPU), a graphical processing unit (GPU), a tensor processing unit (TPU), a video processing unit (VPU) or other type of processing circuitry, as well as portions or combinations of such circuitry elements. 
     The memory  1112  may comprise random access memory (RAM), read-only memory (ROM), flash memory or other types of memory, in any combination. The memory  1112  and other memories disclosed herein should be viewed as illustrative examples of what are more generally referred to as “processor-readable storage media” storing executable program code of one or more software programs. 
     Articles of manufacture comprising such processor-readable storage media are considered illustrative embodiments. A given such article of manufacture may comprise, for example, a storage array, a storage disk or an integrated circuit containing RAM, ROM, flash memory or other electronic memory, or any of a wide variety of other types of computer program products. The term “article of manufacture” as used herein should be understood to exclude transitory, propagating signals. Numerous other types of computer program products comprising processor-readable storage media can be used. 
     Also included in the processing device  1102 - 1  is network interface circuitry  1114 , which is used to interface the processing device with the network  1104  and other system components, and may comprise conventional transceivers. 
     The other processing devices  1102  of the processing platform  1100  are assumed to be configured in a manner similar to that shown for processing device  1102 - 1  in the figure. 
     Again, the particular processing platform  1100  shown in the figure is presented by way of example only, and system  100  may include additional or alternative processing platforms, as well as numerous distinct processing platforms in any combination, with each such platform comprising one or more computers, servers, storage devices or other processing devices. 
     For example, other processing platforms used to implement illustrative embodiments can comprise converged infrastructure. 
     It should therefore be understood that in other embodiments different arrangements of additional or alternative elements may be used. At least a subset of these elements may be collectively implemented on a common processing platform, or each such element may be implemented on a separate processing platform. 
     As indicated previously, components of an information processing system as disclosed herein can be implemented at least in part in the form of one or more software programs stored in memory and executed by a processor of a processing device. For example, at least portions of the functionality for storage cluster load balancing based on predicted performance metrics as disclosed herein are illustratively implemented in the form of software running on one or more processing devices. 
     It should again be emphasized that the above-described embodiments are presented for purposes of illustration only. Many variations and other alternative embodiments may be used. For example, the disclosed techniques are applicable to a wide variety of other types of information processing systems, storage systems, storage clusters, etc. Also, the particular configurations of system and device elements and associated processing operations illustratively shown in the drawings can be varied in other embodiments. Moreover, the various assumptions made above in the course of describing the illustrative embodiments should also be viewed as exemplary rather than as requirements or limitations of the disclosure. Numerous other alternative embodiments within the scope of the appended claims will be readily apparent to those skilled in the art.