Methods for provisioning workloads in a storage system using machine learning and devices thereof
Abstract:
A method, non-transitory computer readable medium, and provisioning advisor device that obtains an intensity and characteristics for each of a plurality of training workloads from storage device volumes. For each of the training workloads, at least first and second training workload parameters are generated, based on the training workload intensity, and an associated training workload signature is generated, based on the training workload characteristics. The first and second training workload parameters and associated training workload signatures are stored in a mapping table. A signature and an intensity for a query workload are obtained. First and second query workload parameters are determined based on a correlation of the query workload signature with the training workload signatures of the mapping table. An estimated latency for the query workload is determined, based on the first and second query workload parameters and the query workload intensity, and the estimated query workload latency is output.
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