Dell Products Inc.

Emc IP Holding Company LLC

Ashish Arvindbhai Pancholi

David C Waser

Bina K Thakkar

A system for generating test cases with workload mixes for a set of target information handling systems includes gathering workload data from a plurality of information handling systems, dividing the workload data into a plurality of workload data bins, identifying workload data characteristics for each workload data bin and identifying workload data sets that may be applicable to a set of target information handling systems. A workload mix may be determined based on workload characteristics of the set of target information handling systems. Real customer workload data including real-time or near real-time workload data may be used to check a test case for accuracy before deploying a test case to a target information handling system.

System and method for automated test case generation based on queuing curve analysis

A methods for identifying a multi-tenant storage array for an application workload includes identifying workload parameters and defining a plurality of groups for each parameter and a plurality of 'bins' corresponding to tuples of the groups. Exemplary workload parameters include a percent read parameter and an I/O size parameter. A bin mix of the workload is determined based on historical data wherein the bin mix indicates bins associated with workload activity exceeding a specified threshold. The bin mix is used to define at least some inputs for a supervised learning model of a process for homing application workloads in a multi-tenant storage array. After appropriate training of the model with a generative adversarial network, the model may be invoked to infer or predict attributes of a suitable storage array. The workload may be associated with a scaling factor that influences the determination of a suitable storage array.

Autonomous workload homing in a multi-tenant environment

A system for generating a workload model for a target information handling system includes gathering workload data from a plurality of information handling systems, dividing the workload data into a plurality of workload data bins, identifying workload data characteristics for each workload data bin and identifying workload data sets that may be applicable to the target information handling system. A workload mix may be determined based on the target information handling systems and workload data characteristics. Real customer workload data including real-time or near real-time workload data may be used to check a workload model for accuracy before deploying the workload model to a target information handling system.

System and method for automated workload identification, workload model generation and deployment

Methods, apparatus, and processor-readable storage media for automatically adjusting storage system configurations in a storage-as-a-service environment using machine learning techniques are provided herein. An example computer-implemented method includes obtaining performance-related data for at least one storage system in a storage-as-a-service environment; processing at least a portion of the obtained performance-related data using one or more rule-based analyses; identifying, based at least in part on results of the processing, one or more storage system configurations, of the at least one storage system, requiring adjustment; determining, using at least one machine learning technique, one or more adjustment amounts for the one or more storage system configurations; and automatically adjusting the one or more storage system configurations, within the storage-as-a-service environment, in accordance with the one or more determined adjustment amounts.

Automatically adjusting storage system configurations in a storage-as-a-service environment using machine learning techniques

Transferring a workload among computing devices is described. For instance, a system can comprise a first device with a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. In an example implementation, a transfer instruction receiving component can receive a transfer instruction from a second device, with the transfer instruction being generated based on a first utilization characteristic assigned to the first device and a second utilization characteristic assigned to a third device. In one or more embodiments, the first utilization characteristic can be based on a workload to provide a service to a client device served by the first device, and the second utilization characteristic can be based on measure of available workload processing capacity for the third device.

Transferring applications from overutilized arrays of computer systems to underutilized arrays of computer systems

Determining drive configurations may include: receiving a data set including tier distributions for data storage systems; applying principal component analysis to the data set to generate a resulting data set having number of dimension in comparison to the data set; determining clusters using the resulting data set, wherein each cluster includes a portion of the tier distributions, wherein each cluster has an associated cluster tier distribution determined in accordance with the portion of the tier distributions in the cluster; selecting one of the clusters; and performing first processing that determines, in accordance with a storage capacity requirement and in accordance with a corresponding cluster tier distribution of the selected one cluster, a drive configuration.

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Cary, NC, United States of America

Ashish Arvindbhai Pancholi