US 12,287,721 B2
Storage management and usage optimization using workload trends
Yan Li, Beijing (CN); Run Qian Bj Chen, Beijing (CN); Chen Guang Zhao, Beijing (CN); Qin Qin Zhou, Beijing (CN); Guang Han Sui, Beijing (CN); Jing Li, Beijing (CN); You Bing Li, Beijing (CN); and Yu Xiang Chen, Shanghai (CN)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Jan. 27, 2022, as Appl. No. 17/585,695.
Prior Publication US 2023/0236946 A1, Jul. 27, 2023
Int. Cl. G06F 11/34 (2006.01); G06F 9/50 (2006.01)
CPC G06F 11/3442 (2013.01) [G06F 9/505 (2013.01); G06F 9/5077 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for managing and optimizing container image storage, the computer-implemented method comprising:
inputting, by a processor, image and data information of a historical workload record into a machine learning model configured to predict future workload requirements based on workload trends of the historical workload record;
learning, by the processor, image and data requirement trends of the historical workload record using the machine learning model;
outputting, by the processor, predicted image and data requirements for future workloads based on the image and data requirement trends learned from the machine learning model;
engaging, by the processor, a checking cycle wherein a daemon process checks whether an image file for an upcoming future workload as predicted by the predicted image and data requirements needs to be downloaded to one or more nodes prior to running the upcoming future workload; and
triggering, by the processor, a pulling task upon a current local time plus a max recorded download time of the image file plus a pre-defined buffer time is less than or equal to a predicted start time of the upcoming future workload.