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

Abstract

In mobile edge computing (MEC), 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 the mobile device. However, the limited resources of the edge server prevent the mobile device from offloading all tasks to the edge servers. To solve the problems, this paper constructs a multi-users and single edge server model for mobile edge computing. In order to minimize the weighted total cost combined energy consumption of mobile devices and task execution delay under the constraints of computing resources 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 the existing algorithms, effectively reducing the weighted total cost of mobile devices and improving the success rate of task execution.