FIELD

This disclosure relates to computing systems and related devices and methods, and, more particularly, to a method and apparatus for estimating a distribution of response times of a storage system, with its particular system characteristics, for a proposed workload.

SUMMARY

The following Summary and the Abstract set forth at the end of this application are provided herein to introduce some concepts discussed in the Detailed Description below. The Summary and Abstract sections are not comprehensive and are not intended to delineate the scope of protectable subject matter, which is set forth by the claims presented below.

All examples and features mentioned below can be combined in any technically possible way.

In some embodiments, a method of estimating a distribution of response times of a storage system for a proposed workload includes obtaining collections of information about operational characteristics of multiple storage systems. Each collection includes parameters describing the configuration of the storage system that was used to create the collection, workload characteristics describing features of the workload that the storage system processed, and storage system response times. For each collection, workload characteristics are aggregated, and the storage system response information is used to train a probabilistic mixture model. The aggregated workload information, storage system characteristics, and probabilistic mixture model parameters of the collections form training examples that are used to train the learning process. In some embodiments, once trained, the learning process is used to regress a probabilistic mixture model from an input proposed workload and proposed storage system configuration, to provide a distribution of response times that would be expected from a storage system having the proposed configuration when processing a workload having characteristics similar to the proposed workload.

