Research on Task Transfer Based on Deep Reinforcement Learning in Mobile Edge Computing
Mobile edge computing extends cloud computing capabilities to the edge of the network to deliver services around mo-bile users.In order to solve the problem of service interruption caused by user movement and limited coverage of edge nodes,this paper proposes a task migration algorithm that integrates the depth deterministic policy gradient of priority experience playback.In this paper,the task processing delay and energy consumption are taken as the optimization objectives,and the task migration and resource allocation problem are modeled as a Markov Decision Process,and the intelligent body makes the resource allocation and migration decision-making scheme according to the proposed policy.The results of simulation experiments show that the designed task migration strategy has superior performance in terms of task execution success rate and delay and energy consumption.
deep reinforcement learningedge computingmobilitytask migration
万方数据
维普
