Abstract: With the popularization of smart mobile devices,a new generation of mobile applications such as face recognition and virtual reality have gradually emerged.The limited computing power and battery capacity of mobile devices cannot support applications with high computing requirements and latency-sensitive applications.Therefore,mobile edge computing(MEC) is proposed to solve this problem.However,in the MEC environment,the reliability of the edge server is low,and the possible equipment failure will lead to the existing offloading decision failure,which increases the application response time and reduces the user experience.In view of the possible failure of edge servers,and considering that the deep deterministic policy gradient(DDPG) algorithm can better deal with the problem of high-dimensional action space through the network fitting strategy function,this paper proposes a server-reliability task offloading based on deep deterministic policy gradient(SRTO-DDPG).The main work is as follows.Firstly,the failure rate of application execution is reduced by duplicating subtasks for secondary offload.Secondly,the task offloading and resource allocation problems with server reliability constraints to minimize application delay are modeled as Markov decision process(MDP).Finally,an algorithm based on DDPG is used to solve the problem.Simulation results show that the SRTO-DDPG strategy can effectively interact with the environment to obtain the optimal offloading decision,and its perfor-mance is better than the local execution strategy(LE).Compared with the single location task offloading based on deep determi-nistic policy gradient(SLTO-DDPG),this strategy can achieve a low total delay of about 26.16% under reliability constraints,and can better adapt to the reliability problems of edge servers in multi-server scenarios.

Key words: Deep reinforcement learning, Dependent tasks, Mobile edge computing, Resource allocation, Task offloading