移动边缘计算中的协同计算卸载策略  

Collaborative Computing Offloading Strategy in Mobile Edge Computing

作　　者：李顺 葛海波 刘林欢 陈旭涛 LI Shun;GE Haibo;LIU Linhuan;CHEN Xutao(School of Electronic Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710121,China)

机构地区：[1]西安邮电大学电子工程学院,西安710121

出　　处：《计算机工程与应用》2022年第21期83-90,共8页Computer Engineering and Applications

基　　金：陕西省自然科学基金(2011JM8038);陕西省重点产业创新链(群)项目(S2019-YF-ZDCXL-0098)。

摘　　要：针对单边缘服务器卸载时导致异地边缘服务器空闲状态下资源浪费问题,在远程云与多个边缘服务器联合卸载的方案下,提出一种基于改进混合粒子群算法的边缘云协同计算卸载策略(cross reorganization PSO,CRPSO)。该卸载策略中以最小化系统总代价(时延和能耗的加权和)为目标建立模型,在粒子群算法中利用适应度对粒子进行优劣分组,通过引入遗传算法中的交叉思想对劣势组的粒子进行取优,由两层筛选机制优化原始种群中粒子,经过算法迭代实现任务的最优卸载策略。仿真结果表明,与Local-MEC算法、ECPSO算法和GCPSO算法相比,所提出的CRPSO算法的系统总代价最小,优化效果明显。Aiming at the problem of resource waste in the idle state of remote edge servers when a single edge server is uninstalled,this paper proposes an edge cloud collaborative computing offloading strategy based on an improved hybrid particle swarm algorithm(cross reorganization PSO,CRPSO).In this offloading strategy,a model is established with the goal of minimizing the total cost of the system(the weighted sum of time delay and energy consumption).In the particle swarm algorithm,the fitness is used to group the advantages and disadvantages of the particles,and the disadvantages are solved by introducing the crossover idea in the genetic algorithm.The group of particles is optimized,the particles in the original population are optimized by a two-layer screening mechanism,and the optimal unloading strategy of the task is achieved through algorithm iteration.The simulation results show that,compared with the Local-MEC algorithm,ECPSO algorithm and GCPSO algorithm,the proposed CRPSO algorithm has the smallest total system cost and the optimization effect is obvious.

关 键 词：移动边缘计算 计算卸载 边云协同 混合粒子群算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]