Dell Products Inc.

Emc IP Holding Company LLC

Jason McCarthy

Girish Warrier

Rongnong Zhou

Francisco Jaen

Lisa O'Mahony

An example methodology includes, by a computing device, determining a recurring time period during which performance data breaches a performance data threshold and applying a data exclusion window on the recurring time period, wherein performance data within the data exclusion window are excluded from system performance calculations. The method also includes, by the computing device, retrieving historical performance data associated with the data exclusion window and determining a percentage of the historical performance data that breach the performance data threshold. The method further includes, responsive to a determination that the percentage of the historical performance data that breach the performance data threshold is within an acceptable breach threshold, removing, by the computing device, the data exclusion window on the recurring time period.

Detection and removal of stale data exclusion windows

A method, comprising: generating a plurality of combined utilization vectors, each of the plurality of combined utilization vectors corresponding to a different one of a plurality of port pairs; updating the plurality of combined utilization vectors based on an expected usage of the storage entity to produce a plurality of updated utilization vectors; selecting one of the plurality of port pairs based on the plurality of updated utilization vectors; and assigning the selected port pair to the replication of data that is stored in the storage entity.

Method and apparatus for performance-based port selection

A method and apparatus for automating creation of multiple simultaneous software development environments on a development machine is provided. Automation code, for example implemented using Python script, reads a text file such as a JSON file describing the development environment to be created. The automation code is configured to locate (locally or in a software repository) and download the supporting software identified in the text file and required to be included in the development environment. The automation code is also configured to determine a set of ports used by the supporting software to communicate with the development machine, and to search for a set of available ports on the development machine that are available and able to be mapped to the development environment. The automation code modifies supporting software configuration files to reflect the port assignments and builds a container including the software code and associated configuration files.

Automating creation of multiple simultaneous software development environments on a computer system

One or more aspects of the present disclosure relate to managing system operations. For example, in embodiments, an input/output (IO) workload can be received by a storage array. Further, one or more array-related operations can be initiated during one or more low IO load points the system workload places on the storage array.

System operations controller

Data migration between a source storage system and a target storage system is automated by calculating the optimal time window or group of consecutive time windows in which to execute the data migration, where optimality is defined in terms of source storage system and target storage system utilizations combined in a manner that accounts for differences between the source storage system and target storage system utilizations. Source and target storage system utilization scores are calculated for each of a plurality of time windows of at least one representative time period. A source-target load score is calculated for each of the time windows based on the source storage system utilization scores and the target storage system utilization scores. At least one of the time windows is selected based on the source-target load scores.

Identifying the best time to schedule a migration based on source and target system load

Storage object groups uniquely associated with respective host applications are processed to model, for each host application, the relationship between current utilized storage capacity of each host application and greatest possible utilized storage capacity of each host application without exhausting either the storage capacity of the storage system or the performance capacity of the storage system. The modeled relationships may be used to calculate headroom and performance impact scores for each host application. Storage object groups that have insufficient headroom for growth, e.g., as indicated by performance impact score, are deemed to be associated with host application workloads that are candidates for migration to a different storage system. The candidates may be ranked and selected for migration based on performance impact scores.

Automatic identification and ranking of migration candidate storage groups based on relative performance impact to current storage array components

A workload planner is used to determine headroom and SLE (Service Level Expectation) compliance of a storage system. The historical workload of the storage system over a set of monitoring intervals is obtained, and optionally a new workload to be moved to the storage system is added to the historical workload. Headroom compliance for components of the storage system, and service level expectation compliance on a per-storage group basis are determined by the workload planner. If a headroom or service level expectation violation is determined for one or more of the monitoring intervals, a set of rules are applied to selectively remove monitoring intervals from the time series. The redacted time series is then used to determine headroom and service level expectation compliance. If no violations are found, based on the redacted time series, the storage system meets headroom and SLE requirements.

Method and apparatus for automatically resolving headroom and service level compliance discrepancies

A method and apparatus for assigning an allocated workload in a data center having multiple storage systems includes selecting one or more storage systems to be assigned the allocated workload based on a combination of performance impact scores and deployment scores. By considering both performance impact and deployment effort, the allocated workload is able to be assigned with a view not only toward storage system performance, but also with a view toward how deployment on a particular storage system would comply with data center policies and the amount of configuration effort it would take to enable the workload to be implemented on the target storage system. This enables workloads to be allocated within the data center while minimizing the required amount of configuration or reconfiguration required to implement the workload allocation within the data center.

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Cambridge, MA, United States of America

Jason McCarthy