ZHAO H W, LYU S K, PANG Z X, et al. A green task offloading strategy for mobile edge computing based on MDP and Q-learning [J]. Journal of Henan Polytechnic University (Natural Science) , 2025, 44(5): 9-16.

DOI: 10.16186/j.cnki.1673-9787.2024070047

Received: 2024/07/10

Revised: 2024/09/29

Published:2025/07/23

A green task offloading strategy for mobile edge computing based on MDP and Q-learning

Zhao Hongwei1,2, Lyu Shengkai1, Pang Zhixi1, Ma Zihan1, Li Yu1

1.School of Information Engineering， Shenyang University， Shenyang  110044， Liaoning， China；2.Institute of Carbon Neutral Technology and Policy， Shenyang University， Shenyang  110044， Liaoning， China

Abstract: Objectives To achieve carbon neutrality in manufacturing industrial Internet companies such as automobile and air conditioner production， edge computing task offloading technology was utilized to address the task offloading problem for production equipment， aiming to reduce the central server load as well as energy consumption and carbon emissions in data centers.  Methods A green edge computing task offloading strategy based on Markov decision process （MDP） and Q-learning was proposed. The strategy accounted for constraints including computing frequency， transmission power， and carbon emissions. Using a cloud-edge-end collaborative computing model， the carbon emission optimization problem was formulated as a mixed integer linear programming model. The model was solved via MDP and Q-learning algorithms. The convergence performance， carbon emissions， and total latency of the proposed method were compared with random allocation， Q-learning， and SARSA algorithms.  Results Compared with existing computation offloading strategies， the proposed task scheduling algorithm demonstrated superior convergence performance， improving by 5% and 2% over the SARSA and Q-learning algorithms， respectively. The system’s carbon emission cost was reduced by 8% and 22% compared to Q-learning and SARSA algorithms， respectively. As the number of terminals increased， the new strategy continued to outperform， achieving carbon emission reductions of 6% and 7% compared to the Q-learning and SARSA algorithms. In terms of total system computation latency， the proposed strategy significantly outperformed other methods， with reductions of 27%， 14%， and 22% compared to the random allocation， Q-learning， and SARSA algorithms， respectively.  Conclusions The proposed task offloading strategy effectively optimized computation task distribution and resource allocation in mobile edge computing scenarios. It striked a balance between latency and energy consumption while significantly reducing system carbon emissions， making it a promising solution for green edge computing.

Key words:carbon emission;edge computing;reinforcement learning;Markov decision process;task offloading