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:
Dec. 12, 2023

Filed:

Jun. 16, 2022
Applicant:

Netapp, Inc., San Jose, CA (US);

Inventor:

Tyler W. Cady, Denver, CO (US);

Assignee:

NetApp, Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 3/16 (2006.01); G06F 13/00 (2006.01); G06F 3/06 (2006.01); G06F 16/182 (2019.01); G06N 3/08 (2023.01);
U.S. Cl.
CPC ...
G06F 3/0655 (2013.01); G06F 3/0604 (2013.01); G06F 3/067 (2013.01); G06F 16/182 (2019.01); G06N 3/08 (2013.01);
Abstract

Systems and methods are described for using a Deep Reinforcement Learning (DRL) agent to automatically tune Quality of Service (QoS) settings of a distributed storage system (DSS). According to one embodiment, a DRL agent is trained in a simulated environment to select QoS settings (e.g., a value of one or more of a minimum IOPS parameter, a maximum IOPS parameter, and a burst IOPS parameter). The training may involve placing the DRL agent into every feasible state representing combinations of QoS settings, workload conditions, and system metrics for a period of time for multiple iterations, and rewarding the DRL agent for selecting QoS settings that minimize an objective function based on a selected measure of system load. The trained DRL agent may then be deployed to one or more DSSs to constantly update QoS settings so as to minimize the selected measure of system load.


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