Lyapunov-Guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks

Opportunistic computation offloading is an effective method to improve the computation performance of mobile-edge computing (MEC) networks under dynamic edge environment. In this paper, we consider a multi-user MEC network with time-varying wireless channels and stochastic user task data arrivals in...

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Vydané v:	IEEE transactions on wireless communications Ročník 20; číslo 11; s. 7519 - 7537
Hlavní autori:	Bi, Suzhi, Huang, Liang, Wang, Hui, Zhang, Ying-Jun Angela
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.11.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Arrivals Computation offloading Computational modeling Data processing Decisions Deep learning deep reinforcement learning Edge computing Frames (data processing) Linear programming Lyapunov optimization Machine learning Mixed integer Mobile computing Mobile edge computing Nonlinear programming Optimization Queues Resource allocation Resource management Stability analysis Stochastic processes Task analysis Wireless communication
ISSN:	1536-1276, 1558-2248
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