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Tingyan LONG, Peng CHEN, Yunni XIA, et al., “A Deep Deterministic Policy Gradient-based Method for Enforcing Service Fault-tolerance in MEC,” Chinese Journal of Electronics, vol. 33, no. 5, pp. 1–11, 2024 doi: 10.23919/cje.2023.00.105
Citation: Tingyan LONG, Peng CHEN, Yunni XIA, et al., “A Deep Deterministic Policy Gradient-based Method for Enforcing Service Fault-tolerance in MEC,” Chinese Journal of Electronics, vol. 33, no. 5, pp. 1–11, 2024 doi: 10.23919/cje.2023.00.105

A Deep Deterministic Policy Gradient-based Method for Enforcing Service Fault-tolerance in MEC

doi: 10.23919/cje.2023.00.105
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  • Author Bio:

    Tingyan LONG received the M.S. degree in School of Computer Science & Technology from Guizhou University, in 2019, and the Ph.D. degree in College of Computer Science from Chongqing University in 2023. Currently, she works at the School of Computer Science and Technology, Guizhou University, Guiyang, China. Her research interests include fault-tolerant, edge computing and reinforcement learning method. (Email: l5469369@hotmail.com)

    Peng CHEN IEEE member, received the B.E. degree in Computer Science and Technology from University of Electronic Science and Technology of China, Chengdu, China in 2001, the M.S. degree in Computer Software and Theory from Peking University, Beijing, China in 2004 and Ph.D. degree in Computer Science and Technology from Sichuan University, Chengdu, China in 2017. He is currently a professor of School of Computer and Software Engineering, Xihua University, Chengdu, China. His research interests include machine learning, service computing and time series analysis. (Email: chenpeng@mail.xhu.edu.cn)

    Yunni XIA IEEE member, received the B.S. degree in Computer Science from Chongqing University in 2003, and the Ph.D. degrees in Computer Science from Peking University (PKU) in 2008. He is currently a professor in the College of Computer Science at Chongqing University, China. He is the author or co-author of more than 100 research publications. His research interests are in Petri nets, software quality, performance evaluation, edge computing and cloud computing system dependability. (Email: xiayunni@hotmail.com)

    Yong MA received the M.S. degree in Computer Science from Xidian University, in 2003, and the Ph.D. degree in Computer Science from Wuhan University in 2006. In 2018, he worked on the integrated control and dispatching of energy in microgrid with Malardalens University, Sweden. He is now a professor in the School of Computer Information Engineering, Jiangxi Normal University. His current research focuses on cloud computing, edge computing, and data science. (Email: mywuda@126.com)

    Xiaoning SUN received the B.S. degree in Computer Science from Chongqing University, China, in 2015, and the Ph.D. degree in Software Engineering from Chongqing University, China, in 2022. Since July 2022, she has been a lecturer with the School of Computer and Information Science, Chongqing Normal University. Her research interests are in service computing, performance evaluation, and edge computing. (Email: sxiaoning@hotmail.com)

    Jiale ZHAO received the B.E. degree in Information Security from Huaibei Normal University, Huaibei, China in 2017, the M.S. degree in Computer Technology from Jiangxi Normal University, Nanchang, China in 2021. He is currently studying for a Ph.D. in Computer Science and Technology at Chongqing University, Chongqing, China. His research interests include fault-tolerance, edge computing and cloud computing. (Email: zhaojiale0415@163.com)

    Yifei LYU is currently pursuing the master's degree with the College of Computer Science, Chongqing University, China. His main research interests are in the areas of cloud computing, edge computing and reinforcement learning method. (Email: yuzhe1334021@163.com)

  • Corresponding author: Email: chenpeng@mail.xhu.edu.cn; Email: xiayunni@hotmail.com
  • Received Date: 2022-03-22
  • Accepted Date: 2022-03-22
  • Available Online: 2024-02-02
  • Mobile edge computing (MEC) provides edge services to users in a distributed and on-demand way. Due to the heterogeneity of edge applications, it is a key challenge for service providers to deploy latency and resource-intensive applications on resource-constrained devices. This is especially true when underlying edge infrastructures are fault and error-prone. In this paper, we propose a fault tolerance approach named DFGP, for enforcing mobile service fault-tolerance in MEC. It synthesizes a generative optimization network (GON) model for predicting resource failure and a deep deterministic policy gradient (DDPG) model for yielding preemptive migration decisions. We show through extensive simulation experiments that DFGP is more effective in fault detection and guaranteeing quality of service (QoS), in terms of fault detection accuracy, migration efficiency, task migration time, task scheduling time, and energy consumption than other existing method.
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