A Task Offloading Decision and Resource Allocation Strategy Based on DDPG in Mobile Edge Computing

Abstract

In mobile edge computing, the mobile device can offload tasks to the server at the edge of the mobile network for execution, thereby reducing the delay of task execution and the energy consumption of mobile device. However, limited resource of the edge server prevents the mobile device from offload all tasks to the edge servers. To solve the problems, a multi-users and single edge server model for mobile edge computing is constructed in this paper. In order to minimize the weighted total cost combined energy consumption of mobile device and task execution delay under the constraints with computing resource and storage resource of the edge server, we propose a task offloading decision and resource allocation algorithm based on improved deep deterministic policy gradient (DDPG) – PERDDPG.  In our algorithm, a special reward function is designed to get the reward value for correlating negatively with the total cost. We can obtain the lowest total cost when the algorithm reaches the maximum reward value. Furthermore, we apply prioritized experience replay (PER) to improve DDPG. So, the PERDDPG has a more dynamic MEC scenario for making offloading decisions and computing resource allocation. Simulation results show that the proposed algorithm can get a better convergence speed and improve the cumulative reward compared to theexisting algorithms, effectively reduce the weighted total cost of mobile devices and improve the success rate of task execution.