RESOURCE MANAGEMENT WITH AGGREGATED RECOMMENDATION
개요
발명자
Xiaotang SHAO; Navin Kumar JAMMULA; Zihan JIANG; Hui LUO; Sen LIN; Chun-Che PENG; Shreyas BADIGER MAHADEV; Estela RAMIREZ RAMIREZ; Yuxuan ZHU
IPC 분류
CPC 분류
Certain aspects of the disclosure pertain to resource management with aggregated recommendation. Recommendations from multiple sources are aggregated and applied to allocate resources for applications deployed in a cluster. Short-term recommenders, including vertical and horizontal pod autoscalers, monitor applications and provide real-time recommendations. Long-term recommenders analyze metrics over longer windows, such as weeks, to provide stable forecasts. Further, long-term recommenders can employ machine-machine learning to infer recommendations from historical data. A global updater aggregates recommendations from both short and long-term recommenders to produce an aggregate recommendation. A resource configuration can be generated from the aggregate recommendation and deployed to a cluster to update resource allocation.
원문 (중국어)
Certain aspects of the disclosure pertain to resource management with aggregated recommendation. Recommendations from multiple sources are aggregated and applied to allocate resources for applications deployed in a cluster. Short-term recommenders, including vertical and horizontal pod autoscalers, monitor applications and provide real-time recommendations. Long-term recommenders analyze metrics over longer windows, such as weeks, to provide stable forecasts. Further, long-term recommenders can employ machine-machine learning to infer recommendations from historical data. A global updater aggregates recommendations from both short and long-term recommenders to produce an aggregate recommendation. A resource configuration can be generated from the aggregate recommendation and deployed to a cluster to update resource allocation.