Distributed Edge Computing Resource Allocation Algorithm Based on DRL in LTE Access Network

With the advent of IoT, as an extension of the traditional cloud-centric computing model, the edge computing model solves the network channel congestion caused by the increasing number of IoT devices and satisfies theirs low latency requirements. Since the edge network needs to adopt heterogeneous structure to meet multiple access requirements during construction, how to properly allocate limited edge resources to process computing requests from mobile edge terminals becomes a challenge under heterogeneous networks. In recent years, reinforcement learning shine in various types of decision-making problems, it has brought new ideas to solve complex decision problems. In this paper, we use reinforcement learning to solve the resource allocation problem of heterogeneous edge server (ES Edge Server) collaborative computing. In this model, we consider the multitasking and the heterogeneity and mobility of edge devices, abstracting the problem into Markov Decision Processes (MDPs) based on the real-time state of the network and the attributes of the task, using based on Actor Critic and Policy Gradient’s DDPG (Deep Deterministic Policy Gradient) make resource allocation optimization decisions. The simulation results show that compared with the DQN-based algorithm, the DDPG-based algorithm has better simulation results in the experiment.