Abstract: Forecasting workload activity for data stored on a data storage device includes selecting at least one metric for measuring workload activity, providing at least one grouping of portions of the data according to a workload affinity determination provided for each of the portions at a subset of a plurality of time steps, where the workload affinity determination is based on each of the data portions in the group experiencing above-average workload activity during same ones of the subset of the plurality of time steps, the subset corresponding to at least one business cycle for accessing the data, and forecasting workload activity for all of the portions of data in the group based on forecasting workload activity for a subset of the data portions that is less than all of the data portions.

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: Described are techniques for processing service level objectives. A first service level objective specified for a storage group of devices may include a first value denoting a first target level of performance for I/O operations. A second service level objective specified for a first portion of the storage group may include a second value denoting a second target level of performance for I/O operations directed to the first portion. The second value may denote a higher level of performance than the first value. It may be determined whether there is a violation of any of the first service level objective and the second level objective. Responsive to determining there is the violation of any of the first service level objective and the second level objective, one or more data movements in accordance with the violation may be performed.

Abstract: Identifying data for placement in a storage system having a plurality of storage classes includes subdividing the data into portions, for each of the portions, independently determining at least one score for a particular portion based on a metric corresponding to access of the particular portion, where the at least one score for the particular portion is independent of scores for other ones of the portions, and identifying sub-portions of data for placement in a particular storage class based on the at least one score of a portion of data corresponding to the sub-portions. The at least one score may be based on short term access statistics and long term access statistics. The access statistics may include read misses, writes, and prefetches.

Abstract: Described are techniques for configuring data storage. Criteria identifying limits is received for a plurality of storage tiers for one or more logical devices. The storage tiers include a first storage tier and one or more other storage tiers where the first storage tier is higher performing than the other storage tiers. First processing is performed to determine a first set of data portions of the logical devices to be stored on physical devices of the first storage tier subject to limits included in said criteria. First processing includes determining scores for data portions of the logical devices. Each score is calculated using one or more metrics including a first metric weighted based on an expected I/O size. The data portions are ranked in accordance with the scores. The first set of data portions stored in the first storage tier is selected based on the score ranking.

Abstract: When migrating data, a first message is received at a target data storage system from a source data storage system. The target data storage system includes a data storage optimizer that performs automated data movement optimizations. The first message requests a reservation of a first amount of storage on a first storage tier for performing a data migration to migrate data from the source to the target data storage system. A first capacity limit of the first storage tier is reduced by the first amount thereby representing the reservation of the first amount of storage for performing the data migration. If the first storage tier does not include an amount of available storage of at least the first amount, processing is performed to increase the amount of available storage of the first storage tier.

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: Techniques for grouping data portions are disclosed. Each group includes data portions determined to exhibit similar behavior. The techniques may include determining whether an affinity measurement with respect to two groups exceeds an affinity threshold; merging the two groups into a single group responsive to the affinity measurement exceeding the affinity threshold; modeling movement of at least one data portion of the single group between two storage tiers at a particular time of day using predicted workload metrics; and performing the data movement of the at least one data portion between the two storage tiers. Predicted workload metrics may be determined by revising first modeled workload metrics using a bias value, where bias values are associated with different times of day, and the bias value is selected based on the particular time of day that the predicted workload metrics are modeling.

Abstract: Described are techniques for performing data storage optimizations. A reserved workload for a first of a plurality of storage tiers is determined. Each of the plurality of storage tiers is characterized by a set of one or more attributes. The first storage tier includes performance characteristics which are any of incomplete, unknown, and unable to be predictively modeled for various workloads. The plurality of storage tiers includes the first tier and a remaining set of additional storage tier(s). Performance is modeled of a first workload distributed among the remaining set of storage tiers. The first workload represents a total workload less the reserved workload. One or more data movements are determined in accordance with the modeling. Each data movement moves a data portion from a first physical device of one of the plurality of storage tiers to a second physical device of another of the plurality of storage tiers.

Abstract: Described are modeling techniques. In accordance with one or more criteria, a determination may be made as to whether to use a dynamic model or a static model to model performance of components, such as storage devices, of a data storage system. A system may include first and second data storage systems where the first data storage system includes a computer readable medium with first code that performs processing in connection with data storage movement optimizations using one or more models including a dynamic model, and second code that generates and maintains the dynamic model used to model performance of storage devices. The second code may include code for performing first processing to determine device sets each of which does not share back-end resources of the second data storage system with any other device sets, and performing second processing to determine sets of performance curves corresponding to the device sets.

Abstract: Managing data in a storage system having a plurality of classes of storage includes determining an amount of data to be provided on at least one of the classes of storage according to a policy, dynamically setting a threshold according to the amount of data to be provided on the at least one of the classes or an expected performance based on the threshold, and placing data on particular classes of storage based on the threshold. Dynamically setting a threshold may include sorting data portions according to at least one score thereof and may include determining a particular score corresponding to the amount of data to be provided. The data portions may be provided in a histogram having a horizontal scale corresponding to a score value and a vertical scale corresponding to a number of data portions having a particular value.

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 performing data storage optimizations. A data storage optimizer on a first data storage system connected to a set of other data storage systems receives performance classification information. The first data storage system includes a first set of storage devices of a first performance classification. The set of other data storage systems includes a second set of storage devices of a second performance classification different from the first performance classification. A write operation is received from the host to write first data to a target address of a virtually provisioned device. It is determined that physical storage is not currently allocated for the target address of the virtually provisioned device. A first portion of physical storage is allocated for storing the first data. The first portion of physical storage is included in a storage device of the second set having the second performance classification.

Abstract: Described are techniques for tracking activity for one or more devices. Each of the one or more devices is partitioned into one or more extents. Each of the one or more extents includes a plurality of sub extents. Each of the sub extents includes a plurality of chunks. For each of said one or more extents of each of said one or more devices, extent activity level information is determined including one or more metrics indicating an activity level for said each extent; and For each of said one or more extents of each of said one or more devices, an activity bitmap is determined. The activity bitmap includes an entry for each sub extent included in said each extent, said entry indicating an activity level for said each sub extent.

Abstract: Described is data storage system modeling. Received at a first data storage system is information representing a workload for I/O operations directed to a logical devices having storage provisioned on physical devices of a second data storage system. Information representing the workload may be obtained by performing discovery processing to discover performance characteristics of the physical devices of the second data storage system. Discovery processing may include receiving, at the first data storage system, I/O operations from a client directed to the set of one or more logical devices having storage provisioned on the physical devices of the second data storage system. The I/O operations are then issued to the second data storage system. In response, performance data is obtained at the first data storage system representing the workload for the plurality of I/O operations. Performance of the second data storage system is modeled in accordance with the workload.

Abstract: Described are techniques for tracking activity for one or more devices. Each of the one or more devices is partitioned into one or more extents. Each of the one or more extents includes a plurality of sub extents. Each of the sub extents includes a plurality of chunks. For each of said one or more extents of each of said one or more devices, extent activity level information is determined including one or more metrics indicating an activity level for said each extent; and For each of said one or more extents of each of said one or more devices, an activity bitmap is determined. The activity bitmap includes an entry for each sub extent included in said each extent, said entry indicating an activity level for said each sub extent.