文献类型：期刊文献

英文题名：Multi-agent DRL for joint completion delay and energy consumption with queuing theory in MEC-based IIoT

作者：Wu, Guowen[1];Xu, Zhiqi[1];Zhang, Hong[1];Shen, Shigen[2,3];Yu, Shui[4]

机构：[1]Donghua Univ, Sch Comp Sci & Technol, Shanghai 201620, Peoples R China;[2]Huzhou Univ, Sch Informat Engn, Huzhou 313000, Peoples R China;[3]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China;[4]Univ Technol Sydney, Sch Comp Sci, Sydney, NSW, Australia

年份：2023

卷号：176

起止页码：80

外文期刊名：JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING

收录：SCI-EXPANDED(收录号：WOS:000971244900001)、、EI(收录号：20231013677165)、Scopus(收录号：2-s2.0-85149278228)、WOS

基金：Acknowledgments This work was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LZ22F020002.

语种：英文

外文关键词：Task offloading; Queuing theory; Industrial Internet of things; Multi -agent deep reinforcement learning; Mobile edge computing

外文摘要：In the Industrial Internet of Things (IIoT), there exist numerous sensor devices with weak computing power and small energy storage. To meet the real-time and big data computing requirements of industrial production, EIIoT (Edge computing-based IIoT) that combines mobile edge computing with IIoT has emerged. It is necessary to offload computing tasks to nearby edge servers for data storage and processing in EIIoT, thus inevitably causing the edge servers to overload. To this end, we propose a jointly constrained optimization model of delay and energy consumption based on queuing theory; this model can effectively solve the task offloading problem in EIIoT. Subsequently, to satisfy the unique offloading requirements of EIIoT, we improve the MAPPO (multi agent proximal policy optimization) algorithm structure to form a lightweight optimal task offloading algorithm called Multi-Agent Deep Reinforcement Learning based on Queuing theory (MAQDRL), which is more suitable for EIIoT. In the algorithm, we systematically integrate queuing theory and use Multi-Agent Deep Reinforcement Learning (MADRL) to obtain the optimal offloading strategy in dynamic and random multiuser offloading environments. We also improve the structure of neural networks of MADRL by analyzing the structural characteristics of the input data. As a result, the algorithm that we proposed exhibits good convergence and exceptional performance in terms of the task arrival rate, bandwidth, energy consumption, latency and other indicators. The simulation results indicate that compared with other classical algorithms, MAQDRL is effective for solving the EIIoT offloading problem. (c) 2023 Elsevier Inc. All rights reserved.