Online Scheduling Optimization for DAG-Based Requests Through Reinforcement Learning in Collaboration Edge Networks

The wide-adoption of edge computing promotes the scheduling of tasks in complex requests upon smart devices on the network edge, whereas tasks are necessary to be offloaded to the cloud when they are intensive in computational and energy resources. Traditional techniques explore mostly the schedulin...

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Veröffentlicht in:	IEEE Access Jg. 8; S. 72985 - 72996
Hauptverfasser:	Zhang, Yaqiang, Zhou, Zhangbing, Shi, Zhensheng, Meng, Lin, Zhang, Zhenjiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2020 Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	[SCCO.COMP]Cognitive science/Computer science Cloud computing Cognitive science Collaboration Computer science Edge computing Electrical engineering. Electronics. Nuclear engineering Electronic devices Energy consumption Energy sources Internet of Things Learning Markov processes Mobile handsets Online DAG-based request optimization Optimization Processor scheduling Quality of experience Scheduling Servers Task analysis Task complexity Task scheduling temporal-difference learning TK1-9971 Temporal-difference learning Online DAG-based request optimization Internet of Things Edge computing
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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