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Edge computational task offloading scheme using reinforcement learning for IIoT scenario

Authors
Hossain, Md. SajjadNwakanma, Cosmas IfeanyiLee, Jae MinKim, Dong-Seong
Issue Date
Dec-2020
Publisher
ELSEVIER
Keywords
Edge computing; Industrial IoT; Offloading; Reinforcement learning
Citation
ICT EXPRESS, v.6, no.4, pp.291 - 299
Journal Title
ICT EXPRESS
Volume
6
Number
4
Start Page
291
End Page
299
URI
https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/18535
DOI
10.1016/j.icte.2020.06.002
ISSN
2405-9595
Abstract
In this paper, end devices are considered here as agent, which makes its decisions on whether the network will offload the computation tasks to the edge devices or not. To tackle the resource allocation and task offloading, paper formulated the computation resource allocation problems as a sum cost delay of this framework. An optimal binary computational offloading decision is proposed and then reinforcement learning is introduced to solve the problem. Simulation results demonstrate the effectiveness of this reinforcement learning based scheme to minimize the offloading cost derived as computation cost and delay cost in industrial internet of things scenarios. (C) 2020 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.
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KIM, DONG SEONG
전자공학부
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