Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.