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: Described are techniques user to determine expected service levels. A first set of one or more data portions are selected from one or more devices forming a first device set. The first set of data portions are stored on physical storage of a physical device set of one or more physical devices having an unknown service level expectation. I/O operations directed to the first set of data portions are received and serviced for a time period. First information is collected that characterizes performance of the physical device set during the time period. In accordance with the first information and using an adaptive learning technique, a first service level expectation for servicing I/Os directed to the physical device set is determined.

Abstract: Described are techniques for controlling performance. A forecast error Et for time period “t” denoting a difference between a predicted performance value Ft of a metric for time period “t” and an actual performance value At of the metric for time period “t” is determined. A tracking signal Tt for time period “t” is determined in accordance with “N” forecast errors. It is determined, in accordance with the tracking signal Tt for time period “t”, whether to modify a current value for alpha denoting a weight used in calculating predicted performance values for the metric. If it is determined to modify the current value for alpha, the current value for alpha is set to a revised value in accordance with the tracking signal Tt for time period “t”. A predicted performance value Ft+1 of the metric for time period “t+1” is determined in accordance with the current value for alpha.

Abstract: Promotion and demotion of extents for each storage pool is determined based on a storage pool state defined by demanded activity and demanded capacity. Promotion and demotion of extents for each storage group is determined based on whether the storage group is performing at SLO, above SLO or below SLO. Events are identified and stored in local records using hash tables. The locally stored events are used to update a database of historical extent activity. A moving average of extent activity is used to calculate the demanded activity.

Abstract: Described are techniques for partitioning processors in a system. At a first point in time, a short term workload ratio and a long term workload ratio are determined. At the first point in time, the system is in a first workload state having a first configuration including a first portion of the processors performing front end processing tasks and a second portion of the processors performing back end processing tasks. A first value is determined where the first value is an absolute value of a difference between the short term workload ratio and the long term workload ratio. First processing is performed that determines, in accordance with the first value and the short term workload ratio, whether to adjust any of the first portion of the processors and the second portion of the processors of the first configuration.

Abstract: Techniques are described data storage optimization that determine predicted values for I/O statistics using an ARIMA (auto-regressive integrated moving average) model. The ARIMA model may be used to capture periodic patterns and trends of workload I/O access to predict the future load demand. A current set of I/O statistics is collected for a current time period T. Using the current set and one or more ARIMA models, a predicted set of I/O statistics is determined for a next time period T+1. Each of the ARIMA models is characterized by model parameters including P denoting a number of auto-regressive terms, D denoting a number of nonseasonal difference needed for stationarity, and Q denoting a number of lagged forecast errors of prediction. A data storage optimizer may determine one or more data portions for movement from a current storage tier to a target storage tier using the predicted set of I/O statistics.

Abstract: Techniques are described for determining I/O workload. A first device of a first data storage system and a second device of a second data storage system are configured as synchronous mirrored devices of a first logical device. The host issues I/O operations to the first logical device over first and second paths. First I/O workload information is determined for a first data portion of the first logical device. Second I/O workload information is determined for the first data portion. The first I/O workload information and the second I/O workload information each include a first number of read operations that is a sum of read operations directed to the first logical device over both the first path and the second path. Data storage optimizations are performed on the first data storage system using the first I/O workload information and/or the second data storage system using the second I/O workload information.

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: Processing I/O operations is described. A write operation writes first data to a first location on a logical device having a logical address space partitioned into extents. The first location is included in a first subrange of the logical address space. Each extent includes logical address subranges of the logical address space. The first subrange is one of the logical address subranges of a first extent of the logical device. Physical storage is allocated from a first physical device of a first write endurance classification of flash memory-based storage devices. The first write endurance classification is selected in accordance with a ranking of multiple write endurance classifications. The physical storage is mapped to the first subrange. The first data is stored on the allocated physical storage. First workload information for the first write endurance classification for the first extent is updated to reflect the first write operation.

Abstract: Processing I/O operations is described. A write operation writes first data to a first location on a logical device having a logical address space partitioned into extents. The first location is included in a first subrange of the logical address space. Each extent includes logical address subranges of the logical address space. The first subrange is one of the logical address subranges of a first extent of the logical device. Physical storage is allocated from a first physical device of a first write endurance classification of flash memory-based storage devices. The first write endurance classification is selected in accordance with a ranking of multiple write endurance classifications. The physical storage is mapped to the first subrange. The first data is stored on the allocated physical storage. First workload information for the first write endurance classification for the first extent is updated to reflect the first write operation.

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 storing data on disk drives. Each disk drive platter of each of the disk drives is partitioned into two separate regions whereby a pivot point denoting a radial distance from the center of each disk drive platter is a boundary partitioning each disk drive platter into the two separate regions. A data portion is classified in accordance with an expected activity level of the data portion. Storage is allocated for the data portion in one of the two separate regions of one of the disk drive platters of one of the disk drives in accordance with the expected activity level of the data portion and a defined allocation flow.

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: A networked data storage system includes data movement logic and a virtual machine for executing a data management processing plan which is generated by a facility outside the storage array based on statistical information associated with extent level activity metrics provided by the storage array. Dynamically updated and automatically generated data management processing plans are provided to the storage array in the form of units of compiled byte code which the storage array is able to verify and execute using a virtual machine. The virtual machine interfaces with the environment in which it is embedded in a carefully constrained manner, thereby preventing clearly undesirable operations.

Abstract: Described are techniques for storing data on disk drives. Each disk drive platter of each of the disk drives is partitioned into two separate regions whereby a pivot point denoting a radial distance from the center of each disk drive platter is a boundary partitioning each disk drive platter into the two separate regions. A data portion is classified in accordance with an expected activity level of the data portion. Storage is allocated for the data portion in one of the two separate regions of one of the disk drive platters of one of the disk drives in accordance with the expected activity level of the data portion and a defined allocation flow.

Abstract: A write operation writes first data to a target logical address range included in a first logical address range of a virtually provisioned device. It is determined that physical storage is not allocated for the target logical address range. First processing is performed to allocate a first portion of physical storage having a first corresponding logical address subrange that includes the target logical address range. The first portion is allocated from a physical device included in a first storage tier. The first processing includes selecting the first storage tier from multiple storage tiers in accordance with selection criteria including metrics characterizing an expected workload or level of activity for the first corresponding logical address subrange of the virtually provisioned device. Mapping information is updated to indicate that the first corresponding logical address subrange of the virtually provisioned device maps to the first portion of physical storage.

Abstract: Techniques are described for determining data movements. A first plurality of performance goals for a plurality of storage pools and a second plurality of performance goals for a plurality of applications are received. A first I/O classification characterizing a workload of a first data portion is determined. The first I/O classification is one of a predetermined set of I/O classifications. A proposed data movement of the first data portion from a first to a second of the plurality of storage pools is determined in accordance with criteria including a match between the first I/O classification and one of the predetermined set of I/O classifications which is preferred for the second storage pool over one or more other I/O classifications of the predetermined set. The criteria also includes any of the first plurality of performance goals and the second plurality of performance goals.

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.