Dependent Task Offloading for Edge Computing based on Deep Reinforcement Learning

Edge computing is an emerging promising computing paradigm that brings computation and storage resources to the network edge, hence significantly reducing the service latency and network traffic. In edge computing, many applications are composed of dependent tasks where the outputs of some are the i...

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Veröffentlicht in:	IEEE transactions on computers Jg. 71; H. 10; S. 2449 - 2461
Hauptverfasser:	Wang, Jin, Hu, Jia, Min, Geyong, Zhan, Wenhan, Zomaya, Albert Y., Georgalas, Nektarios
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Communications traffic Computation offloading Deep learning deep reinforcement learning Edge computing Energy consumption Graphical representations Heuristic methods Machine learning Multi-access edge computing Neural networks Reinforcement learning sequence to sequence neural networks Solid modeling Task analysis task offloading Training Wireless communication
ISSN:	0018-9340, 1557-9956
Online-Zugang:	Volltext
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