面向移动边缘计算的任务卸载方法研究

Research on Task Offloading Strategy for Mobile Edge Computing

目前大多计算卸载策略是在任务整体卸载情况下进行的,且仅考虑时延或能耗单一指标,未将二者结合进行优化,为此,以任务处理的时延与能耗加权和为优化目标,提出一种基于强化学习的部分卸载算法。将单个任务的处理分为本地计算和部分卸载两种方式,且在部分卸载中引入了变量确定卸载权重,最后利用强化学习Q-learning完成了所有任务的计算卸载与资源分配。实验结果表明,所提算法能有效降低任务处理的时延与能耗。

Computation offloading strategy in mobile edge computing can help users decide how to execute tasks,which is related to user experience,and has become a research hotspot in mobile edge computing.At present,most computation offloading strategies are carried out under the condition of overall task offloading,and only consider a single indicator of delay or energy consumption,and do not combine the two for optimization.To solve this problem,this paper takes the weighted sum of task processing delay and energy consumption as the optimization goal,and proposes a partial offloading algorithm based on reinforcement learning.We divide the processing of a single task into local computing and partial offloading computing,and introduce a variable to determine the offloading weight in partial offloading.Finally,we use reinforcement learning Q-learning to complete the computation offloading and resource allocation of all tasks.Experimental results show that the proposed algorithm can effectively reduce the delay and energy consumption of task processing.