Energy-efficient multiuser and multitask computation offloading optimization method

Intelligent and Converged Networks

article

Meini Pan¹  Zhihua Li¹  Junhao Qian³ 

[1] School of Artificial Intelligence and Computer Science;Jiangnan University;School of IoT Engineering,CHINA. Jiangnan University

关键词: Mobile Edge Computing (MEC);    computation offloading;    optimization model;    Reinforcement Learning (RL);   

DOI  :  10.23919/ICN.2023.0007

学科分类：社会科学、人文和艺术（综合）

来源: TUP

【 摘 要 】

For dynamic application scenarios of Mobile Edge Computing (MEC), an Energy-efficient Multiuser and Multitask Computation Offloading (EMMCO) optimization method is proposed. Under the consideration of multiuser and multitask computation offloading, first, the EMMCO method takes into account the existence of dependencies among different tasks within an implementation, abstracts these dependencies as a Directed Acyclic Graph (DAG), and models the computation offloading problem as a Markov decision process. Subsequently, the task embedding sequence in the DAG is fed to the RNN encoder-decoder neural network with combination of the attention mechanism, the long-term dependencies among different tasks are successfully captured by this scheme. Finally, the Improved Policy Loss Clip-based PPO2 (IPLC-PPO2) algorithm is developed, and the RNN encoder-decoder neural network is trained by the developed algorithm. The loss function in the IPLC-PPO2 algorithm is utilized as a preference for the training process, and the neural network parameters are continuously updated to select the optimal offloading scheduling decisions. Simulation results demonstrate that the proposed EMMCO method can achieve lower latency, reduce energy consumption, and obtain a significant improvement in the Quality of Service (QoS) than the compared algorithms under different situations of mobile edge network.

【 授权许可】

CC BY-NC-ND