Joint computation offloading and resource allocation for D2D-assisted mobile edge computing

Jiang, W., Feng, D., Sun, Y. , Feng, G., Wang, Z. and Xia, X.-G. (2023) Joint computation offloading and resource allocation for D2D-assisted mobile edge computing. IEEE Transactions on Services Computing, 16(3), pp. 1949-1963. (doi: 10.1109/TSC.2022.3190276)

Abstract

Computation offloading via device-to-device communications can improve the performance of mobile edge computing by exploiting the computing resources of user devices. However, most proposed optimization-based computation offloading schemes lack self-adaptive abilities in dynamic environments due to time-varying wireless environment, continuous-discrete mixed actions, and coordination among devices. The conventional reinforcement learning based approaches are not effective for solving an optimal sequential decision problem with continuous-discrete mixed actions. In this paper, we propose a hierarchical deep reinforcement learning (HDRL) framework to solve the joint computation offloading and resource allocation problem. The proposed HDRL framework has a hierarchical actor-critic architecture with a meta critic, multiple basic critics and actors. Specifically, a combination of deep Q-network (DQN) and deep deterministic policy gradient (DDPG) is exploited to cope with the continuous-discrete mixed action spaces. Furthermore, to handle the coordination among devices, the meta critic acts as a DQN to output the joint discrete action of all devices and each basic critic acts as the critic part of DDPG to evaluate the output of the corresponding actor. Simulation results show that the proposed HDRL algorithm can significantly reduce the task computation latency compared with baseline offloading schemes.

• Item Type: Articles
• Additional Information: This work was supported in part by the National Science and Technology Major Project under Grant 2020YFB1807601, and in part by the Shenzhen Science, and Technology Program under Grants JCYJ20210324095209025, and in part by ZTE Industry-Academia Research Cooperation Funds.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sun, Dr Yao and Feng, Professor Gang
• Authors: Jiang, W., Feng, D., Sun, Y., Feng, G., Wang, Z., and Xia, X.-G.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Services Computing
• Publisher: IEEE
• ISSN: 2372-0204
• ISSN (Online): 1939-1374
• Published Online: 13 July 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Transactions on Services Computing 16(3):1949-1963
• Publisher Policy: Reproduced in accordance with the publisher copyright policy