Computation Offloading Algorithm Based on Deep Reinforcement Learning and Multi-Task Dependency for Edge Computing

Tengxiang Lin, Cheng Kuan Lin, Zhen Chen, Hongju Cheng*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Edge computing is an emerging promising computing paradigm that brings computation and storage resources to the network edge, significantly reducing service latency. In this paper, we aim to divide the task into several sub-tasks through its inherent interrelation, guided by the idea of high concurrency for synchronization, and then offload sub-tasks to other edge servers so that they can be processed to minimize the cost. Furthermore, we propose a DRL-based Multi-Task Dependency Offloading Algorithm (MTDOA) to solve challenges caused by dependencies between sub-tasks and dynamic working scenes. Firstly, we model the Markov decision process as the task offloading decision. Then, we use the graph attention network to extract the dependency information of different tasks and combine Long Short-term Memory (LSTM) with Deep Q Network (DQN) to deal with time-dependent problems. Finally, simulation experiments demonstrate that the proposed algorithm boasts good convergence ability and is superior to several other baseline algorithms, proving this algorithm’s effectiveness and reliability.

Original languageEnglish
Title of host publicationNew Trends in Computer Technologies and Applications - 25th International Computer Symposium, ICS 2022, Proceedings
EditorsSun-Yuan Hsieh, Ling-Ju Hung, Sheng-Lung Peng, Ralf Klasing, Chia-Wei Lee
PublisherSpringer Science and Business Media Deutschland GmbH
Pages111-122
Number of pages12
ISBN (Print)9789811995811
DOIs
StatePublished - 2022
Event25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022 - Taoyuan, Taiwan
Duration: 15 Dec 202217 Dec 2022

Publication series

NameCommunications in Computer and Information Science
Volume1723 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022
Country/TerritoryTaiwan
CityTaoyuan
Period15/12/2217/12/22

Keywords

  • Computation offloading
  • Deep reinforcement learning
  • Dependency
  • Edge computing
  • Multiple tasks

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