河南理工大学学报（自然科学版）2025，Vol.44Issue(5)：9-16,8.DOI:10.16186/j.cnki.1673-9787.2024070047
基于MDP和Q-learning的绿色移动边缘计算任务卸载策略
A green task offloading strategy for mobile edge computing based on MDP and Q-learning
摘要
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 ad-dress 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 ac-counted 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,improv-ing 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 reduc-tions of 6%and 7%compared to the Q-learning and SARSA algorithms.In terms of total system computa-tion 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.Con-clusions The proposed task offloading strategy effectively optimized computation task distribution and re-source allocation in mobile edge computing scenarios.It striked a balance between latency and energy con-sumption while significantly reducing system carbon emissions,making it a promising solution for green edge computing.关键词
碳排放/边缘计算/强化学习/马尔可夫决策过程/任务卸载Key words
carbon emission/edge computing/reinforcement learning/Markov decision process/task offloading分类
信息技术与安全科学引用本文复制引用
赵宏伟,吕盛凱,庞芷茜,马子涵,李雨..基于MDP和Q-learning的绿色移动边缘计算任务卸载策略[J].河南理工大学学报（自然科学版）,2025,44(5):9-16,8.基金项目
国家自然科学基金资助项目(71672117) （71672117）
东北地质科技创新中心区创新基金资助项目(QCJJ2023-49) （QCJJ2023-49）
