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

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

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

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.

原文English
主出版物標題New Trends in Computer Technologies and Applications - 25th International Computer Symposium, ICS 2022, Proceedings
編輯Sun-Yuan Hsieh, Ling-Ju Hung, Sheng-Lung Peng, Ralf Klasing, Chia-Wei Lee
發行者Springer Science and Business Media Deutschland GmbH
頁面111-122
頁數12
ISBN(列印)9789811995811
DOIs
出版狀態Published - 2022
事件25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022 - Taoyuan, 台灣
持續時間: 15 12月 202217 12月 2022

出版系列

名字Communications in Computer and Information Science
1723 CCIS
ISSN(列印)1865-0929
ISSN(電子)1865-0937

Conference

Conference25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022
國家/地區台灣
城市Taoyuan
期間15/12/2217/12/22

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