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:
Oct. 19, 2021

Filed:

Aug. 31, 2016
Applicant:

Emc Corporation, Hopkinton, MA (US);

Inventors:

Fabiano C. Botelho, Sunnyvale, CA (US);

Mark Chamness, Menlo Park, CA (US);

Dmitry Serdyuk, St. Petersburg, RU;

Guilherme Menezes, Santa Clara, CA (US);

Assignee:

EMC IP HOLDING COMPANY LLC, Hopkinton, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/20 (2019.01); G06F 12/02 (2006.01); G06F 3/06 (2006.01); G06N 20/10 (2019.01);
U.S. Cl.
CPC ...
G06F 12/0253 (2013.01); G06F 3/065 (2013.01); G06F 3/067 (2013.01); G06F 3/0608 (2013.01); G06F 3/0619 (2013.01); G06F 3/0641 (2013.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01); G06F 2212/702 (2013.01);
Abstract

A first set of garbage collection (GC) features and non-GC features associated with a storage system are received, the first set of features being associated with a predetermined start date and a time window. A learning equation is generated having a plurality of vectors of GC features and a plurality of vectors of non-GC features. For a current iteration representing a current GC process, it is determined whether a first prior GC process was started within the time window. An entry of vectors of the non-GC features of the learning equation is populated based on corresponding feature values of the first set of non-GC features, in response to determining that the first prior GC process was started within the time window. A predetermined regression algorithm is applied to the learning equation to generate a GC duration predictive model to predict a GC duration of a subsequent GC process.


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