STORAGE MANAGEMENT AND USAGE OPTIMIZATION USING WORKLOAD TRENDS
Solutions preparing container images and data for container workloads prior to start times of workloads predicted through workload trend analysis. Local storage space on the node is managed based on workload trends, optimizing local storage of image files without requiring frequent reloading and/or...
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| creator | Li, You Bing Chen, Run Qian Bj Zhao, Chen Guang Li, Jing Zhou, Qin Qin Li, Yan Chen, Yu Xiang Sui, Guang Han |
| description | Solutions preparing container images and data for container workloads prior to start times of workloads predicted through workload trend analysis. Local storage space on the node is managed based on workload trends, optimizing local storage of image files without requiring frequent reloading and/or deletion of image files, avoiding network intensive I/O operations when pulling images to local storage by workload scheduling systems. Systems perform collection of historical data including image and workload properties; analyze historical data for workload trends, including predicted start times, image files needed, number of nodes and types of nodes. Based on predicted future workload start times, nodes are selected from an ordered list of node requirements and workload properties. Selected nodes' local storage is managed using predicted future start times of workloads, to avoid removing image files having sooner start times, while removing (as needed) images files predictively utilized for workloads further into the future. |
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Local storage space on the node is managed based on workload trends, optimizing local storage of image files without requiring frequent reloading and/or deletion of image files, avoiding network intensive I/O operations when pulling images to local storage by workload scheduling systems. Systems perform collection of historical data including image and workload properties; analyze historical data for workload trends, including predicted start times, image files needed, number of nodes and types of nodes. Based on predicted future workload start times, nodes are selected from an ordered list of node requirements and workload properties. 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Local storage space on the node is managed based on workload trends, optimizing local storage of image files without requiring frequent reloading and/or deletion of image files, avoiding network intensive I/O operations when pulling images to local storage by workload scheduling systems. Systems perform collection of historical data including image and workload properties; analyze historical data for workload trends, including predicted start times, image files needed, number of nodes and types of nodes. Based on predicted future workload start times, nodes are selected from an ordered list of node requirements and workload properties. Selected nodes' local storage is managed using predicted future start times of workloads, to avoid removing image files having sooner start times, while removing (as needed) images files predictively utilized for workloads further into the future.</abstract><oa>free_for_read</oa></addata></record> |
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| title | STORAGE MANAGEMENT AND USAGE OPTIMIZATION USING WORKLOAD TRENDS |
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