Patent Abstract:
A method of predictive baseline volume profile creation for new volumes in a networked storage system and a system for dynamically reevaluating system performance and needs to create an optimized and efficient use of system resources by changing volume profiles as necessary. The system gathers statistical data and analyzes the information through algorithms to arrive at an optimal configuration for volume clusters. Clusters are then reallocated and reassigned to match the ideal system configuration for that point in time. The system continually reevaluates and readjusts its performance to meet throughput requirements specified in the quality of service agreement

Full Description:
[0001]     This application claims the benefit of U.S. Provisional Application No. 60/497,913, filed Aug. 27, 2003, the disclosure of which is herein incorporated by reference in its entirety. 
     
    
     FIELD OF INVENTION  
       [0002]     The present invention relates to storage systems. More particularly, the present invention relates to allocation and reallocation of clusters to volumes for greater efficiency and performance in a storage system.  
       BACKGROUND OF THE INVENTION  
       [0003]     With the accelerating growth of Internet and intranet communication, high-bandwidth applications (such as streaming video), and large information databases, the need for networked storage systems has increased dramatically. The key apparatus in such a networked storage system is the storage controller. One primary function of storage controllers in a networked storage system is to assume the responsibility of processing storage requests so that the host processors are free to perform other processing tasks. Storage controllers manage all of the incoming, outgoing, and resident data in the system through specialized architectures, algorithms, and hardware. However, it should also be recognized that there is also a need for high performance non-networked storage systems. Thus, while this application consistently discusses network storage systems, it should be recognized that the invention may also be practiced by non-networked storage systems. More particularly, the storage controller of the present invention also may be adapted for non-networked storage systems.  
         [0004]     Typical storage controller systems use cluster allocation and volume mapping of those clusters to manage data, I/O, and other administrative tasks within the networked storage system. Clusters reside on volumes formed of a portion of a disk drive or many disk drives in a redundant array of independent disks (RAID) storage architecture. Clusters are typically identical in size; however, each may be assigned to a different RAID architecture. Their physical locations are stored in volume maps, which are updated as new clusters are allocated or deleted. Clusters provide system granularity and aid in the transfer and management of large quantities of data by breaking them down into smaller quantities of data.  
         [0005]     The storage system is monitored by one or more data collection mechanisms to evaluate system performance and compare the current performance output to the required output, which is usually outlined in a Quality of Service (QoS) contract. The statistical data gathered by the statistics collection system facilitates achievement of a desired QoS.  
         [0006]     In a networked storage system, it is critical that the system perform to a given QoS. In general, each host that accesses the networked storage system establishes a service level agreement (SLA) that defines the minimum guaranteed bandwidth and latency that the host can expect from the networked storage system. The SLA is established to ensure that the system performs at the level specified in the QoS contract.  
         [0007]     QoS, redundancy, and performance requirements may not be met after the system has been running for a certain period because the volume profiles that define the system configuration are static and were created prior to system launch. Therefore, any deviation in the types and amounts of data to be processed may affect system performance. In other words, system needs may change over time and, as a result, performance may drop. Many RAID storage architectures account for this decrease in productivity by over-provisioning the system. Over-provisioning is accomplished by increasing the number of drives in the system. More drive availability in the system means more storage space to handle inefficient use of the existing system resources. This solution, however, is a waste of existing system resources and increases costs.  
         [0008]     U.S. Pat. No. 6,487,562, “DYNAMICALLY MODIFYING SYSTEM PARAMETERS IN DATA STORAGE SYSTEM,” describes a system and method for dynamically modifying parameters in a data storage system such as a RAID system. Such parameters include QoS parameters, which control the speed at which system operations are performed for various parts of a data storage system. The storage devices addressable as logical volumes can be individually controlled and configured for preferred levels of performance and service. The parameters can be changed at any time while the data storage system is in use, with changes taking effect very quickly. These parameter changes are permanently stored and therefore allow system configurations to be maintained. A user interface allows a user or system administrator to easily observe and configure system parameters, preferably using a graphic user interface (GUI) that allows a user to select system changes along a scale from minimum to a maximum.  
         [0009]     The method described in the &#39;562 patent offers a solution to over-provisioning in a RAID architecture by introducing a GUI and using external human intervention. While this saves physical disk drive and hardware costs, the costs are now transferred to paying a person to manage and operate the system on a daily basis. Furthermore, the system is prone to human error in the statistical data analysis of the system performance and, as a result, the system may not be filly optimized.  
         [0010]     Therefore, it is an object of the present invention to provide a method of optimizing system resources and capabilities in a networked storage system.  
         [0011]     It is another object of the present invention to provide a method of configuring system resources that improves system performance.  
         [0012]     It is yet another object of the present invention to provide a means to eliminate the need for over-provisioning in a networked storage system.  
         [0013]     It is yet another object of the present invention to provide a means to decrease cost in a networked storage system by efficiently utilizing existing system resources.  
       SUMMARY OF THE INVENTION  
       [0014]     The present invention incorporates QoS mechanisms, fine-grain mapping, statistical data collection systems, redundThe present invention incorporates QoS mechanisms, fine-grain mapping, statistical data collection systems, redundancy requirements, performance measurements, and statistical analysis algorithms to provide a means for predicting volume profiles and dynamically reconfiguring those profiles for optimum performance in a networked storage system. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0015]     The foregoing and other advantages and features of the invention will become more apparent from the detailed description of exemplary embodiments of the invention given below with reference to the accompanying drawings, in which:  
         [0016]      FIG. 1  is a flow diagram of a predictive and dynamically reconfigurable volume profiling method;  
         [0017]      FIG. 2  is a flow diagram of an asynchronous cluster allocation method;  
         [0018]      FIG. 3  is a flow diagram of a background reallocation and optimization method;  
         [0019]      FIG. 4  shows an example I/O density histogram; and  
         [0020]      FIG. 5  is a block diagram of a storage system interfaced to a network having two hosts.  
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT  
       [0021]     Now referring to the drawings, where like reference numerals designate like elements, there is shown in  FIG. 5 a  block diagram of a storage system  500  in accordance with the principles of the present invention. The storage system  500  includes a first interface  1010  for managing host communications and a second interface  1011  for managing communications with one or more storage devices  2000 . The storage devices  2000  may comprise a plurality of clusters (not illustrated) which are each comprised of a plurality of sectors (not illustrated). The storage controller  1000  also includes a memory  1020 . The controller  1000  may also comprise one or more functional units (not illustrated), which collectively manage the storage. At least some of the functional units may have access to the memory  1020 . As illustrated, the storage system  500  is a networked storage system since the storage system  500  communicates to hosts  4000  over a network  3000 . However, interface  1010  may also be a non-network interface, and hosts  4000  may communicate directly with the storage system via interface  1010 . Thus, the present invention is also applicable to non-networked storage systems.  
         [0022]      FIG. 1 a  flow diagram of a predictive and dynamically reconfigurable volume profiling method  100 . The method  100  is executed by the controller  1000  and operates as described below:  
         [0023]     Step  110 : Establishing Volume Profile  
         [0024]     In this step, a new volume profile, known as the baseline profile, is created for each new volume. Every volume in the system has a baseline profile created for it as it comes online. New volumes are created in the system when new drives are added, when old volumes are deleted and reused, or when the system is running for the first time.  
         [0025]     The baseline volume profile includes information about the size of the volume, the number of drives in the volume, the number of clusters needed to define the volume, the RAID types of those clusters, and their preferred location in relation to the radius or diameter of the disk. Clusters located closer to the outer (i.e., larger) radius are higher-performance clusters than those located toward the inner (i.e., smaller) radius of the disk because the disk inherently spins faster at the outer radius than it does at the innermost radius. The clusters outlined in the baseline volume may or may not be allocated. Clusters that have been allocated also have their disk location stored in the baseline profile. Clusters that have not yet been allocated have only their RAID type stored in the baseline volume profile. In most cases, however, baseline volume profiles do not contain clusters allocated to physical storage space. This allocation occurs later, when the cluster is required for a write action.  
         [0026]     The baseline profile is created using predictive algorithms based on QoS requirements, redundancy requirements, the size of the volume, the number of drives per volume, the read/write activity (I/O) that will likely address the volume, the likely amount of data to be read from or written to the volume, and the performance expectations. Method  100  proceeds to step  120 .  
         [0027]     Step  120 : Storing Current State of Volume Profile  
         [0028]     In this step, the most current volume profile is stored as a table in memory  1020  so that other system resources may access the information. Method  100  proceeds to step  130 .  
         [0029]     Step  130 : Collecting Volume Statistics  
         [0030]     In this step, a statistical data collection system begins to gather volume statistics, i.e., information related to host commands. The information may include, for example, total number of read sectors, total number of write sectors, total number of read commands, total number of write commands, and system latency time associated with each read and write command. In one exemplary embodiment, the information is recorded in an I/O density histogram. An exemplary I/O density histogram is illustrated in  FIG. 4 . In one exemplary embodiment, the statistical collection system is the one which is described in a U.S. application Ser. No. 10/______ (Attorney Docket A7995.0012/P012), filed Nov. 17, 2003, entitled “METHOD OF COLLECTING AND TALLYING OPERATIONAL DATA USING AN INTEGRATED I/O CONTROLLER IN REAL TIME,” which is hereby incorporated by reference in its entirety.  
         [0031]     The data collection system continues to record data from time zero and aggregates the data into the I/O density histogram. At any time, the system may reset the I/O density histogram and begin recording data from that point on. The I/O density histogram is available to other system resources for analyzing and making decisions based on its data. Method  100  proceeds to step  140 .  
         [0032]     Step  140 : Does Volume Profile Need to be Updated? 
         [0033]     In this decision step, algorithms are used to analyze the statistical data in the I/O density histogram and to compare the results to the current state of the volume profile. The matrix shown in  FIG. 2  illustrates example performance-to-configuration decisions that may be made based on the statistical data analysis. For example, a particular cluster may have many more write transactions than read transactions. It should be noted that while clusters are used in the description herein, the present invention may also be practiced by applying the I/O density histogram to storage units other than clusters. In higher capacity storage systems, it may be useful to apply the I/O density histogram to larger allocation units. In general, the present invention may be practiced by applying the I/O density histogram to any type of subvolume granularity, and the size of the subvolume granularity may also be a programmable or configurable quantity. The system may decide that a RAID with redundancy through mirroring (e.g., RAID 10) cluster would be more appropriate than the currently allocated RAID with redundancy through parity (e.g., RAID 5) cluster and that the volume profile should be updated. On the other hand, for example, a RAID 5 cluster may have large numbers of sequential data burst transfers in its histogram and, therefore, the system may decide that the original RAID 5 assignment is correct for that particular cluster. If the volume profile needs to be updated, method  100  proceeds to step  150 ; if not, method  100  returns to step  130 .  
         [0034]     Step  150 : Updating Volume Profile  
         [0035]     In this step, method  100  updates the current volume profile with the decision made in step  140 . For example, clusters of one RAID type may be changed to a different RAID type, clusters at inner diameter disk locations may be moved to outer diameter locations. The current volume profile no longer matches the actual system configuration at this point. Other asynchronous methods described in reference to  FIG. 3  and  FIG. 4  perform the task of matching the system configuration to that of the current volume profile. Method  100  returns to step  130 .  
         [0036]      FIG. 2  is an example I/O density histogram  200 . Data is collected by a system that records all transaction requests for a given volume. Histogram  200  includes data such as the total volume read commands, total volume write commands, number of read sectors for each cluster, number of write sectors for each cluster, etc. Alternately, totals collected by volume region may have courser granularity, where a region is some number of contiguous logical clusters. This may also change the bin size of histogram  200 .  
         [0037]     The data aggregates from time zero; more data continues to be incorporated as time increases. Histogram  200  is used by method  100  to determine whether a volume profile needs to be updated based on the statistical information contained therein. Method  100  may reset histogram  200  at any time and start a new data collection for another example I/O density histogram  200 , perhaps altering histogram  200  granularity. Moreover, method  100  may utilize different types of statistical data depending on system needs. For example, statistical data may include queue depth data or command latency data for a given functional unit of the controller  1000 .  
         [0038]      FIG. 3  is a flow diagram of a cluster allocation method  300 .  
         [0039]     Step  310 : Evaluating Current State of Volume Profile  
         [0040]     In this step, the controller  1000  evaluates the current state of the volume profile stored in memory. From the current state volume profile, the controller  1000  knows which clusters have been allocated and which may need to be reserved so that the cluster allocator may allocate them later. Method  300  proceeds to step  320 .  
         [0041]     Step  320 : Is New Cluster Needed? 
         [0042]     In this decision step, the controller  1000  evaluates the need for reserving new cluster pointers that coincide with the cluster configurations in the volume profile. Additionally, the controller  1000  may determine that a new cluster is needed due to a message from the cluster free list that it is empty or below threshold. Finally, a system request may trigger the need for a new cluster if a host requests a write to a volume with no cluster allocation. If the controller needs to create a new cluster, method  300  proceeds to step  330 ; if not, method  300  returns to step  310 .  
         [0043]     Step  330 : Evaluating System Resources  
         [0044]     In this step, the controller  1000  looks at system resources to determine where space is available for the new cluster. The controller  1000  scans for any new drives in the system and checks to see if any clusters that have been deleted are ready for reallocation. Method  300  proceeds to step  340 .  
         [0045]     Step  340 : Is Adequate Apace Available? 
         [0046]     In this decision step, the controller  1000  determines whether there is physical storage space available for the new cluster identified in step  320 . If so, method  300  proceeds to step  350 ; if not, method  300  proceeds to step  370 . In one exemplary embodiment, the controller  1000  includes a functional unit known as a cluster manager (not illustrated), and steps  310 ,  320 , and  330  are executed by the cluster manager.  
         [0047]     Step  350 : Allocating New Cluster  
         [0048]     In this step, the controller  1000  removes a cluster pointer from the head of the appropriate cluster free list and allocates the cluster to its respective volume. Since the allocation process is asynchronous from the cluster reservation process, the cluster allocation may occur at any time after the reservation has been made and does not necessarily follow step  340  chronologically. The controller  1000  sends a message to the cluster manager that the cluster has been allocated and no longer has a status of “reserved”. Method  300  proceeds to step  360 .  
         [0049]     Step  360 : Updating Volume Profile  
         [0050]     In this step, the cluster controller  1000  updates the volume profile to reflect that a cluster has been allocated. Additional information regarding the position and location of the newly allocated cluster are also added to the volume profile. The new profile is stored in memory as the current volume profile. Method  300  returns to step  310 . In one exemplary embodiment, the controller  1000  includes a functional unit known as a cluster allocator (not illustrated), and steps  350  and  360  are executed by the cluster allocator.  
         [0051]     Step  370 : Generating Error Message  
         [0052]     In this step, the system is notified by the controller  1000  that there was an error reserving the requested cluster pointer. Reasons for the failure are recorded in the error message. Method  300  returns to step  310 .  
         [0053]      FIG. 4  is a flow diagram of a background cluster reallocation and optimization method  400 . Method  400  is a background process that runs when there is an opportunity. Method  400  does not have priority over any other system transactions and, therefore, does not contribute to system latency.  
         [0054]     Step  410 : Evaluating Current Volume Profile  
         [0055]     In this step, the system reviews the current state of a volume profile stored in memory and observes the currently allocated clusters and their locations as well as the types of clusters that are in the volume profile. Method  400  proceeds to step  420 .  
         [0056]     Step  420 : Is Existing Allocation Different from Profile? 
         [0057]     In this decision step, the system compares the existing allocation of clusters for a particular volume to the optimized cluster allocation in the volume profile and determines whether they are the same. If yes, method  400  proceeds to step  430 , if no, method  400  returns to step  410 .  
         [0058]     Step  430 : Is New Allocation Feasible? 
         [0059]     In this decision step, the system evaluates its resources to determine whether the new, optimal cluster allocation is feasible given the current state of the system. If yes, method  400  proceeds to step  440 ; if no, method  400  returns to step  410 .  
         [0060]     Step  440 : Reallocating Clusters  
         [0061]     In this step, clusters are reallocated to the optimal type defined by the volume profile. Method  400  returns to step  410 .  
         [0062]     While the invention has been described in detail in connection with the exemplary embodiment, it should be understood that the invention is not limited to the above disclosed embodiment. Rather, the invention can be modified to incorporate any number of variations, alternations, substitutions, or equivalent arrangements not heretofore described, but which are commensurate with the spirit and scope of the invention. Accordingly, the invention is not limited by the foregoing description or drawings, but is only limited by the scope of the appended claims.

Technology Classification (CPC): 6