US 12,229,033 B2
Method, device, and program product for managing computing resource in storage system
Bing Liu, Tianjin (CN); and Lingdong Weng, Beijing (CN)
Assigned to EMC IP HOLDING COMPANY LLC, Hopkinton, MA (US)
Filed by EMC IP Holding Company LLC, Hopkinton, MA (US)
Filed on Oct. 5, 2020, as Appl. No. 17/063,274.
Claims priority of application No. 202010787951.3 (CN), filed on Aug. 7, 2020.
Prior Publication US 2022/0043732 A1, Feb. 10, 2022
Int. Cl. G06F 11/34 (2006.01); G06F 3/06 (2006.01); G06F 11/14 (2006.01); G06F 11/30 (2006.01); G06F 18/214 (2023.01)
CPC G06F 11/3414 (2013.01) [G06F 3/0619 (2013.01); G06F 3/0631 (2013.01); G06F 3/0659 (2013.01); G06F 3/0673 (2013.01); G06F 11/1451 (2013.01); G06F 11/1461 (2013.01); G06F 11/3034 (2013.01); G06F 11/3419 (2013.01); G06F 11/3433 (2013.01); G06F 18/214 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A method for managing a computing resource in a storage system, comprising:
receiving a processing request for processing a task using the computing resource;
acquiring, based on a usage state of the computing resource, a length of time required for processing the task;
determining, based on a load model of the computing resource and a current workload of the computing resource, a future workload prediction of the computing resource for processing a future data access request for the storage system within a future time period, the load model describing an association relationship between a previous load and a subsequent load of the computing resource for processing a historical data access request for the storage system;
generating a workload curve describing an association relationship between the future workload prediction and a time point in the future time period;
selecting a target time period in the future workload prediction that corresponds to a reduced workload, on the workload curve, of the computing resource within the future time period and matches the length of time required for the processing of the task, wherein selecting the target time period based on the workload curve comprises:
establishing a sliding window based on the length of time; and
determining the target time period based on the sliding window and the workload curve during movement of the sliding window along a time axis of the workload curve; and
processing the task using the computing resource according to the target time period.