计算机技术与发展2024，Vol.34Issue(4)：116-123,8.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0018
基于深度强化学习的任务卸载和资源分配优化
Joint Optimization of Task Offloading and Resource Allocation Based on Deep Reinforcement Learning
摘要
Abstract
Mobile edge computing(MEC)can provide users with nearby storage and computing services at the edge of the network,so as to bring the advantages of low energy consumption and low delay to mobile users.Aiming at the multi-user and multi MEC scenario based on ultra-dense network(UDN),starting from the user side and aiming at minimizing the total user computing overhead,we solve the problems of user unloading decision,upload transmission power optimization and MEC computing resource allocation in the unloading process.Specifically,considering that the problem is a NP hard MINLP,we decompose the problem into two subproblems and solves it in two stages.Firstly,in the first stage,a task offloading decision based on deep reinforcement learning(DRL)is designed to solve the task unloading sub problem,and then in the second stage,KKT condition and golden section algorithm are used to solve the optimization problems of MEC computing resource allocation and uplink transmission power respectively.Simulation results show that the proposed scheme effectively reduces the user's computing overhead and improves the system performance on the premise of ensuring the user's delay constraint.关键词
超密集网络/移动边缘计算/任务卸载/资源分配/深度强化学习Key words
ultra-dense network/mobile edge computing/task offloading/resource allocation/deep reinforcement learning分类
信息技术与安全科学引用本文复制引用
龚亮亮,张影,张俊尧,许之琛,康彬..基于深度强化学习的任务卸载和资源分配优化[J].计算机技术与发展,2024,34(4):116-123,8.基金项目
国家自然科学基金面上项目(62171232,62071255,62001248) （62171232,62071255,62001248）
国家博士后面上基金(2020M681684) （2020M681684）
江苏省高校自然科学重大项目(20KJA510009) （20KJA510009）
