Abstract: One or more aspects of the present disclosure relate to predicting one or more performance metrics of a storage array configuration. In embodiments, a storage array configuration can be generated based on one or more array-configuration related parameters. In addition, IO workload metrics corresponding to an IO workload received by an array corresponding to the storage array configuration can be recorded. Further, at least one response time (RT) prediction can be generated based on the IO workload metrics. Accordingly, another storage array configuration can be generated based on the RT predictions and according to performance requirements defined by a service level objective (SLO).

Abstract: A complex object generator is implemented, for example, as an integrated development environment. The complex object generator includes a hierarchical object relationship data structure describing classes of objects, relationships between the classes of objects, and metrics associated with the classes of objects. The hierarchical object relationship data structure is parsed by parser to create a hierarchy of Java classes. A user interface uses the hierarchy of Java classes to constrain selection of objects and metrics during creation of the complex object. As input is received relative to selected objects and metrics, the complex object is incrementally built. By constraining object and metric selection using the hierarchy of Java classes, the complex object is guaranteed to be valid when built, thus reducing or eliminating the number of errors associated with building complex objects.

Abstract: A complex object generator is implemented, for example, as an integrated development environment. The complex object generator includes a hierarchical object relationship data structure describing classes of objects, relationships between the classes of objects, and metrics associated with the classes of objects. The hierarchical object relationship data structure is parsed by parser to create a hierarchy of Java classes. A user interface uses the hierarchy of Java classes to constrain selection of objects and metrics during creation of the complex object. As input is received relative to selected objects and metrics, the complex object is incrementally built. By constraining object and metric selection using the hierarchy of Java classes, the complex object is guaranteed to be valid when built, thus reducing or eliminating the number of errors associated with building complex objects.

Abstract: A method includes (a) receiving workload data for a data storage system, the data storage system having a plurality of processing cores, the data storage system being configured to operate according to any of a plurality of profiles, each of the plurality of profiles having a respective assignment of respective cores of the plurality of cores to particular storage roles of the data storage system, (b) for each profile of the plurality of profiles, simulating operation of the data storage system running a workload based on the received workload data using the assignment of respective cores of the plurality of cores to particular storage roles for that profile, thereby producing a set of utilization scores, (c) selecting a profile of the plurality of profiles based on the set of utilization scores, and (d) outputting an indication of the selected profile for use by the data storage system.

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 processing collected workload and performance data. Components of a component category are selected, filtering criteria is specified, the filtering criteria is applied to the collected workload and performance data for the components selected and thereby generating filtered data results, and the filtered results are viewed. The filtering criteria may include a first operation and a first set of one or more metrics included in the collected workload and performance data for the components selected. The first operation may be applied to the first set of one or more metrics and may be selected from a plurality of operations. The plurality of operations may include at least one arithmetic operation, at least one logical operation and at least one relational operation.

Abstract: A method, apparatus and computer program product for providing pre-calculated performance simulation is presented. Performance simulations for a storage system are provided by selecting a subset of system configuration options from a large set of system configuration options. Simulation results for the subset of system configuration options are pre-calculated. The simulation results for the subset of system configuration options are loaded on a portable device. System configuration requirements for a customer are received and at least one system configuration option is selected from the subset of system configuration options based on the simulation results and the system configuration requirements for a customer.

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: A target data storage system is simulated. A source configuration is received. First activity data is received representing workload for the source configuration. A target data storage system configuration for one or more target data storage systems is created. The target data storage system configuration is modeled including determining a simulated workload for components of the target data storage system configuration based on the first activity data. A utilization map is displayed in a user interface indicating component level utilizations of components of the target data storage system configuration based on the simulated workload. A data movement optimization for the target data storage system configuration is simulated. A second utilization map is displayed indicating updated component level utilizations for the target data storage system configuration which have been revised to model the simulated data movement optimization for the target data storage system configuration.

Abstract: A tool and/or other system is provided that simulates write pacing in connection with a requested operation for a storage system to allow an analysis of the effect of the requested operation on the storage system. In an embodiment, the storage system may be an RDF system operating in an asynchronous transfer mode (e.g., an SRDF/A system) and the requested operation may correspond to a snapshot copy operation of the R2 device and/or a slowdown in one of the storage devices and/or the links therebetween while the SRDF/A system is operating. The system described herein enables simulation of a storage system to take into account an effect of write pacing and may be advantageously used to anticipate and/or otherwise minimize or avoid operations at a particular time that would create undesired initiation of write pacing.

Abstract: Described are techniques for identifying a data storage volume exhibiting a performance problem. First information indicating a sorted ordering of a plurality of maximum response times is displayed for a plurality of data storage volume. A first of the plurality of data storage volumes having a largest one of the plurality of maximum response times is selected. In response to such selecting, additional information is displayed in the user interface about the first data storage volume. The additional information includes at least one workload or performance characteristic of the first data storage volume.

Abstract: A method for race detection in parallel software, including performing an execution of software under test (SUT), the SUT having an expected outcome. The method further includes detecting a race condition in the SUT responsive to a first interleaving generated during the execution, replaying the first interleaving to a start-point of the race condition, and imposing a second interleaving at the start-point of the race condition. The method also includes determining a second-execution outcome of the SUT responsive to the second interleaving, and comparing the expected outcome with the second-execution outcome.

Abstract: A method for race detection in parallel software, including performing an execution of software under test (SUT), the SUT having an expected outcome. The method further includes detecting a race condition in the SUT responsive to a first interleaving generated during the execution, replaying the first interleaving to a start-point of the race condition, and imposing a second interleaving at the start-point of the race condition. The method also includes determining a second-execution outcome of the SUT responsive to the second interleaving, and comparing the expected outcome with the second-execution outcome.

Abstract: A method for performing coverability analysis in software, including performing a static analysis of software under test (SUT) so as to identify a plurality of dominating blocks in the SUT, formulating respective coverability tasks for the dominating blocks of the SUT and generating rules regarding behavior of the SUT corresponding respectively to the coverability tasks. The method further includes, for each of the rules, running a symbolic model checker to test a behavioral model of the SUT, so as to produce respective results for the rules, and computing a coverability metric for the SUT responsive to the results and the coverability tasks.