Abstract: Telemetry data gathered from active deployed SAN nodes is used to create a machine learning model that predicts storage system performance, e.g. in terms of response time. The telemetry data may be filtered to remove outlier values and less relevant information before creating the training dataset. Engineered features may be created that include types of data that are not present in the telemetry data. For example, data types from the telemetry data may be combined to create engineered features that are more relevant than the individual data types. The engineered features are included in the training dataset. The machine learning model may be used to test possible configurations for a planned SAN node based on expected workload and performance requirements. Outputted data may include satisfactory configurations for a planned storage system.

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: Telemetry data gathered from active deployed SAN nodes is used to create a machine learning model that predicts storage system performance, e.g. in terms of response time. The telemetry data may be filtered to remove outlier values and less relevant information before creating the training dataset. Engineered features may be created that include types of data that are not present in the telemetry data. For example, data types from the telemetry data may be combined to create engineered features that are more relevant than the individual data types. The engineered features are included in the training dataset. The machine learning model may be used to test possible configurations for a planned SAN node based on expected workload and performance requirements. Outputted data may include satisfactory configurations for a planned storage system.

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: Described are techniques for processing a data request for a device of a data storage system. A data request directed to a device that is a virtualized device of the data storage system is received. The data storage system has a plurality of devices comprising one or more local devices and one or more virtualized devices. Each of the local devices is a storage device included in the data storage system and each of the virtualized devices is an external storage device with respect to the data storage system and is presented as a local storage device of the data storage system. First processing is performed by a disk adapter in connection with servicing the data request for the device. The first processing includes sending the request to an external storage device corresponding to the virtualized device over a connection in accordance with path information obtained from discovery processing.

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 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: Described are techniques for selecting a storage pool configuration. A set of one or more candidate storage pool configurations is determined. For each of the candidate storage pool configurations of the set, a reliability calculation is performed to determine a reliability value indicating reliability of the candidate storage pool configuration. For each of the candidate storage pool configurations of the set, an availability calculation is performed to determine an availability value indicating availability of the candidate storage pool configuration. In accordance with selection criteria, one of the candidate storage pool configurations of the set for a first storage pool is selected. The selection criteria includes the availability value and the reliability value for each of the plurality of candidate storage pool configurations.

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 system for displaying logical structure of heterogeneous source code includes a parser configured to read the source code and to generate parsed code. A code mapper having access to the parsed code is configured to generate from that parsed code a map representative of the logical structure of the heterogeneous source code.