Freyr ^++: Harvesting Idle Resources in Serverless Computing via Deep Reinforcement Learning

Serverless computing has revolutionized online service development and deployment with ease-to-use operations, auto-scaling, fine-grained resource allocation, and pay-as-you-go pricing. However, a gap remains in configuring serverless functions-the actual resource consumption may vary due to functio...

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Veröffentlicht in:	IEEE transactions on parallel and distributed systems Jg. 35; H. 11; S. 2254 - 2269
Hauptverfasser:	Yu, Hanfei, Wang, Hao, Li, Jian, Yuan, Xu, Park, Seung-Jong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.11.2024
Schlagworte:	Attention Deep reinforcement learning Dynamic scheduling Heuristic algorithms incremental learning Memory management Nearest neighbor methods reinforcement learning resource harvesting Serverless computing Training
ISSN:	1045-9219, 1558-2183
Online-Zugang:	Volltext
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