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:
Nov. 29, 2022

Filed:

Mar. 25, 2020
Applicant:

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

Inventors:

Shashikanth Lakshmikantha, San Jose, CA (US);

Sankalp Suhas Taralekar, Santa Clara, CA (US);

Tuan Nguyen, San Jose, CA (US);

Venkata Narasa Kumar Kuchi, San Jose, CA (US);

Koushik Nagaraj Godbole, Santa Clara, CA (US);

Assignee:

EMC IP Holding Company LLC, Hopkinton, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06F 16/11 (2019.01); G06N 3/04 (2006.01); G06F 11/34 (2006.01); G06F 11/30 (2006.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 11/3034 (2013.01); G06F 11/3495 (2013.01); G06F 16/11 (2019.01); G06N 3/0445 (2013.01);
Abstract

Requests from file system services of a storage system are registered. Each file system service, when executed, utilizes one or more resources of the storage system. Each request includes information describing resource requirements required by a respective file system service. Resource utilization data of the resources are collected over a period of time. The resource utilization data includes an identification of a resource, a timestamp, and a measurement indicating a utilization level of the resource corresponding to the timestamp. A machine learning model is trained to predict utilization patterns of the resources. Execution of the file system services are scheduled based on the predicted utilization patterns. Monitoring is conducted during the execution of the file system services. Based on the monitoring a determination is made as to whether the machine learning model should be retrained.


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