US 12,204,791 B2
Automatically tuning a quality of service setting for a distributed storage system with a deep reinforcement learning agent
Tyler W. Cady, Denver, CO (US)
Assigned to NetApp, Inc., San Jose, CA (US)
Filed by NetApp, Inc., San Jose, CA (US)
Filed on Aug. 2, 2023, as Appl. No. 18/364,199.
Application 18/364,199 is a continuation of application No. 17/841,903, filed on Jun. 16, 2022, granted, now 11,842,068.
Application 17/841,903 is a continuation of application No. 17/237,505, filed on Apr. 22, 2021, granted, now 11,392,315, issued on Jul. 19, 2022.
Prior Publication US 2024/0004576 A1, Jan. 4, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 3/06 (2006.01); G06F 16/182 (2019.01); G06N 3/08 (2023.01)
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)] 20 Claims
OG exemplary drawing
 
1. A distributed storage system (DSS) comprising:
one or more processors; and
instructions that when executed by the one or more processors cause a deep reinforcement learning (DRL) agent of the DSS to:
determine, based on a current state of the DSS, whether to update a Quality of Service (QoS) setting of the DSS representing a level of performance being provided by the DSS to a client, wherein the current state includes (i) the QoS setting, (ii) information indicative of a type of workload to which the DSS is exposed, and (iii) a system metric indicative of a load on the DSS; and
after an affirmative determination:
determine an updated QoS setting; and
apply the updated QoS setting.