The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 03, 2022

Filed:

Jan. 20, 2020
Applicant:

Emc Ip Holding Company Llc, Hopkinton, MA (US);

Inventors:

Paulo Abelha Ferreira, Rio de Janeiro, BR;

Adriana Bechara Prado, Niterói, BR;

Pablo Nascimento da Silva, Niterói, BR;

Assignee:

Dell Products, L.P., Hopkinton, MA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 3/06 (2006.01); G06F 11/30 (2006.01); G06K 9/62 (2022.01);
U.S. Cl.
CPC ...
G06F 3/0611 (2013.01); G06F 3/067 (2013.01); G06F 3/0629 (2013.01); G06F 3/0653 (2013.01); G06F 3/0659 (2013.01); G06F 11/3034 (2013.01); G06K 9/6256 (2013.01);
Abstract

A distribution of response times of a storage system can be estimated for a proposed workload using a trained learning process. Collections of information about operational characteristics of multiple storage systems are obtained, in which 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. Once trained, the learning process is used to provide a distribution of response times that would be expected from a storage system having a proposed configuration when processing a proposed workload.


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