Abstract: Described are techniques for modeling processing performed in a data storage system. Inputs received may include a plurality of workloads each denoting a workload for one of a plurality of storage groups, a plurality of service level objectives each denoting a target level of performance for one of the plurality of storage groups, a plurality of capacities each denoting a storage capacity of one of a plurality of storage tiers, and a plurality of maximum workloads each denoting a maximum workload capability of one of the plurality of storage tiers. Using the inputs, placement of data of the plurality of storage groups on the plurality of storage tiers may be modeled. Output(s) may be generated based on the modeling where the output(s) may include an amount of each of the plurality of storage tiers allocated by modeling to each of the plurality of storage groups.

Abstract: A method of cache hit ratio simulation using a partial data set includes determining a set of sampled addresses, the set of sampled addresses being a subset of all addresses of a storage system of a storage environment. The method further includes using, by a simulation engine, a cache management algorithm to determine a cache hit ratio of the sampled addresses, the cache management algorithm being also used by a cache manager to place a portion of the addresses of the storage system into cache during a runtime operation. The method further includes determining a quantity of memory access operations to frequently accessed addresses in the set of sampled addresses, and correcting, by the simulation engine, the cache hit ratio of the sampled addresses based on the quantity of memory access operations to the frequently accessed addresses in the set of sampled addresses. The simulation also handles sequential operations accurately.

Abstract: A method of cache hit ratio simulation using a partial data set includes determining a set of sampled addresses, the set of sampled addresses being a subset of all addresses of a storage system of a storage environment. The method further includes using, by a simulation engine, a cache management algorithm to determine a cache hit ratio of the sampled addresses, the cache management algorithm being also used by a cache manager to place a portion of the addresses of the storage system into cache during a runtime operation. The method further includes determining a quantity of memory access operations to frequently accessed addresses in the set of sampled addresses, and correcting, by the simulation engine, the cache hit ratio of the sampled addresses based on the quantity of memory access operations to the frequently accessed addresses in the set of sampled addresses. The simulation also handles sequential operations accurately.

Abstract: A method of cache hit ratio simulation using a partial data set includes determining a set of sampled addresses, the set of sampled addresses being a subset of all addresses of a storage system of a storage environment. The method further includes using, by a simulation engine, a cache management algorithm to determine a cache hit ratio of the sampled addresses, the cache management algorithm being also used by a cache manager to place a portion of the addresses of the storage system into cache during a runtime operation. The method further includes determining a quantity of memory access operations to frequently accessed addresses in the set of sampled addresses, and correcting, by the simulation engine, the cache hit ratio of the sampled addresses based on the quantity of memory access operations to the frequently accessed addresses in the set of sampled addresses. The simulation also handles sequential operations accurately.

Abstract: Improved techniques for applying performance controls in a data storage system based on overload correlations between units of managed storage objects (UMOs). When a performance indicator (e.g. response time) for a target UMO is outside an acceptable range, a competing UMO that potentially shares at least one potentially shared hardware component with the target UMO is identified. An overload correlation between the target UMO and the competing UMO is generated that indicates an amount of correlation between the performance indicator for the target UMO and a performance indicator for the competing UMO. A performance control is then applied to the throughput of the competing UMO that reduces the throughput of the competing UMO. The amount that the throughput of the competing UMO is reduced is based on the amount of overload correlation between the target UMO and the competing UMO.

Abstract: Dynamic resource partitioning techniques may include receiving a plurality of performance goals for a plurality of consumers, each of the plurality of performance goals denoting a performance goal for a different one of the plurality of consumers; performing first processing to determine whether one or more of a plurality of subsystems are overloaded and whether there is at least a first consumer of the plurality of consumers violates a corresponding one of the plurality of performance goals specified for said first consumer; and responsive to determining that one or more of the plurality of subsystems are overloaded and that at least a first consumer is not meeting the corresponding one of the plurality of performance goals specified for said first consumer, performing second processing to reduce workload directed to each of the one or more subsystems that are overloaded.

Abstract: Control over the overall data cache hit rate is obtained by managing partitioning caching responsibility by address space. Data caches determine whether to cache data by hashing the data address. Each data cache is assigned a range of hash values to serve. By choosing hash value ranges that do not overlap, data duplication can be eliminated if desired, or degrees of overlap can be allowed. Control over hit rate maximization of data caches having best hit response times is obtained by maintaining separate dedicated and undedicated partitions within each cache. The dedicated partition is only used for the assigned range of hash values.

Abstract: A method, computer program product, and computing system for defining one or more computing system requirements for desired data storage array. One or more design criteria is defined for the desired data storage array. At least one recommended data storage array is identified for the desired data storage array based, at least in part, upon the one or more computing system requirements and the one or more design criteria.

Abstract: For each storage array in a storage system, a single value score indicative of the overall health of the respective storage array is calculated. Storage array health score may be tracked over time and used to identify storage arrays in need of maintenance. The storage array health score may be calculated as a composite of four component scores: physical component health, logical component health, Service Level Objective compliance and Best Practice Configuration Compliance. The physical component and logical component scores may be based on multiple different category health scores, and each category health score may be based on multiple instance health scores. Health scores may be used to identify remedial actions and predict health score increase as a result of remedial actions.

Abstract: Described are techniques for determining data storage system configurations. Inputs are provided to a data storage system configuration generator. The inputs include first inputs and second inputs. The first inputs include a performance objective, a storage capacity, and input(s) characterizing an I/O workload. The second inputs characterize performance of data storage system components and costs of the data storage system components. First processing is performed by the data storage system configuration generator in accordance with the inputs to determine a set of one or more candidate data storage system configurations. The data storage system configuration selects, in accordance with one or more criteria, a first candidate data storage system configuration from the set.

Abstract: Determining cumulative workload skew is described. Measurements for one or more logical devices are determined. The set of measurements include, for each of N storage tiers, a workload measurement identifying workload directed to the single tier, and a capacity measurement identifying an amount of data stored in the single tier. N points may be determined using the measurements. Each point corresponds to a different storage tier and has a first coordinate identifying a cumulative percentage of data portions stored in the storage tier and all other tiers having a higher performance ranking than the one storage tier, and a second coordinate denoting an aggregated percentage of total workload directed to the foregoing cumulative percentage of data portions. A curve representing a cumulative workload skew may be determined using these N points and a point of origin.

Abstract: Described are techniques for evaluating data movement alternative. A set of criteria including capacity and performance limits is received. First processing is performed to evaluate a plurality of alternatives for use in data movement with respect to a set of logical devices having data stored on a set of physical storage devices. Each of the plurality of alternatives includes a different set of data movement criteria comprising capacity limits and a different set of performance limits. The set of physical storage devices includes at least a first physical device of one of a plurality of storage tiers and a second physical device of another one of the plurality of storage tier. One of the sets of performance limits is selected in accordance with the first processing.

Abstract: Described are techniques for destaging data. Write data for write operations are stored in cache page(s). The cache may be partitioned into cache pages and write data stored thereon may be marked as write pending denoting that write data needs to be written out to physical storage. Weight values may be determined for the cache pages in accordance with factors. A first cache page may be selected having a highest such weight value where the selected first cache page includes at least some write data marked as write pending. The write data of the selected first cache page may be destaged to physical storage.

Abstract: Determining cumulative workload skew is described. Measurements for one or more logical devices are determined. The set of measurements include, for each of N storage tiers, a workload measurement identifying workload directed to the single tier, and a capacity measurement identifying an amount of data stored in the single tier. N points may be determined using the measurements. Each point corresponds to a different storage tier and has a first coordinate identifying a cumulative percentage of data portions stored in the storage tier and all other tiers having a higher performance ranking than the one storage tier, and a second coordinate denoting an aggregated percentage of total workload directed to the foregoing cumulative percentage of data portions. A curve representing a cumulative workload skew may be determined using these N points and a point of origin.

Abstract: An efficient linear technique is used to determine allocation of tiered storage resources among data extents based on system performance and SLOs. Efficiency is achieved by first determining a system performance boundary condition via hardware performance modeling under desirable system performance zones. SLOs are then balanced and SLO achievement improved by exchanging workload activities among SG donors and SG receivers while system performance boundary conditions are maintained. Remaining unutilized capacity is the uniformly distributed to further improve SLO achievement.

Abstract: The system described herein allows for picking data to be moved from one physical location to another using certain criteria, such as expected activity level, to achieve load balancing or load optimization. Migrating data for a logical device includes subdividing the logical device into subsections, where each of the subsections corresponds to a particular address range of the logical device that is mapped to a physical device containing corresponding data and copying data for a first subsection from a first physical device to a second, different, physical device, where data for a second subsection is maintained indefinitely on the first physical device after copying the data for the first subsection to the second physical device. The first physical device may be provided in a different geographic location than the second physical device.

Abstract: Described are techniques that identify problematic workloads. Measured response times for workloads associated applications are received. Each of the applications has one of the workloads resulting in one of the measured response times for the application. The applications share a set of one or more resources. In accordance with a first set of one or more criteria, it is determined whether there is an occurrence of abnormal performance with respect to performance of the applications. Responsive to determining the occurrence of abnormal performance with respect to performance of the applications, second processing is performed that includes determining, using the measured response times and in accordance with a second set of one or more criteria, an application set of one or more of the applications having an associated workload causing the occurrence of abnormal performance. A remediation may also be taken to address or alleviate the abnormal performance.

Abstract: To facilitate both minimal allocations and adaptive allocations, two sets of storage group policies are defined—one policy setting minimal allocation values for storage group access to storage resources and one policy setting maximal allocation values for storage group access to storage. In addition, a set of priority weights is specified that is used to balance access to storage tiers across storage groups. Upon existence of contention for storage resources, minimum allocation values for storage groups are determined based on the priority weights for the storage groups, resulting in threshold values being set to enable at least partial access to storage resources for all storage groups without requiring priority weighting of the activity density distributions of the competing storage groups. Allocations other than the minimal allocations are provided based on relative activity density distribution of storage extents between groups.

Abstract: Described are techniques for partitioning storage of a data storage system. A plurality of storage groups is selected. Each of the plurality of storage groups includes one or more devices and is associated with an allocation policy including a first threshold specifying one of a minimum or a maximum amount of storage of a first of a plurality of storage tiers. Workload data for the plurality of storage groups is analyzed. Storage of a plurality of storage tiers is partitioned for use by the plurality of storage groups in accordance with criteria. The criteria includes workloads of the plurality of storage groups and the allocation policy associated with each of the plurality of storage groups.

Abstract: The contributions of a virtual storage unit to the utilization of a data storage system may be quantified. A utilization score may be determined for each virtual storage unit for one or more functional components of the data storage system, for example, a front-end adapter, back-end adapter or interface physical storage unit. A utilization score may be determined for the data storage system as a whole by combining the component utilization scores of the virtual storage unit. Component and/or system utilization scores may be visually presented to a user in a manner that enables the user to assess the relative contributions of the virtual storage units to utilization of the component or overall system, respectively. What-if scenarios may be considered using the utilization scores to determine the consequences of moving one or more virtual storage units from one data storage system to another, and a live migration may result.