摘要
针对终端直传(Device-to-Device,D2D)通信技术的移动边缘计算场景中计算卸载的高时延、高能耗问题,提出一种基于多目标优化的计算卸载策略。该计算卸载策略基于时延和能耗多目标优化模型,引入过度卸载问题的分析,对NSGA-II算法进行改进,包括适用于计算卸载的基因编码策略、交叉和变异方法,通过求解帕累托最优来最小化任务执行时间和能耗。此外,还提出一种数据路由算法,以平衡路由设备的传输能耗,并优化路由路径。通过仿真实验,该算法的平均提升效率最高可达41.7%,任务重传率降低至7.8%。实验结果表明,本文提出的算法能明显减少执行时延、能耗,降低任务重传率和提高任务卸载成功率。
Focused on the high latency and energy consumption for computational offload in mobile edge computing scenarios with device-to-device(D2D)communication technology,a computational offloading strategy based on multi-objective optimiza tion is proposed.The strategy is based on a computing offloading model with multi-objective optimization of delay and energy con sumption,introduces the analysis of excessive offloading problem,improves the NSGA-II algorithm,including genetic encoding strategy,crossover and variation methods applicable to computing offloading,and minimizes task execution time and energy con sumption by solving the Pareto optimum.In addition,a data routing algorithm is proposed,which balances the transmission en ergy consumption of routing devices and optimizes the routing paths.Through simulation experiments,the average boosting effi ciency of the algorithm is up to 41.7%and the task retransmission rate is reduced to 7.8%.The experiment results show that the proposed algorithm can significantly reduce the execution delay,energy consumption,task retransmission rate and improve the task offload success rate.
作者
陈琦
李晶晶
CHEN Qi;LI Jing-jing(School of Computer Science,South China Normal University,Guangzhou 510620,China)
出处
《计算机与现代化》
2024年第1期21-28,共8页
Computer and Modernization
基金
国家重点研发计划——科技创新2030—“新一代人工智能”重大项目(2018AAA0101300)。
关键词
终端直传
移动边缘计算
多目标优化
帕累托最优
device-to-device(D2D)
mobile edge computing
multi-objective optimization
Pareto optimization
