Novel Resource Allocation Algorithm of Edge Computing Based on Deep Reinforcement Learning Mechanism
Degan Zhang, Hongrui Fan, Jie Zhang · 2021
Edge computing is a computing paradigm that can bring practical value to most modern enterprises. When it is integrated into the Internet of Things system, it can improve the QoS of mobile applications, and can realize real-time management of the generated big data.5G promotes the development of the edge computing paradigm further, and mobile users can obtain low-latency and high-speed access QoS. In this paper, we study the resource allocation method of edge servers in the MEC environment. We are absorbed in solving the problem of allocation of limited resources in the MEC system, such as computing resources and available bandwidth, with the goal of maximizing the average resource utilization and task processing capacity of edge servers in the MEC system, while considering some differential processing for delay-sensitive applications, describe this resource allocation problem as a Finite-state Markov Decision Process (FMDP), and consider the continuity of the user state, and design a new Edge Computing Resource Allocation Algorithm based on Deep Deterministic Policy Gradient (ECRAA-DDPG)to find the optimal strategy for resource allocation. Finally, a large number of experiments are used to prove the performance of the new algorithm, and the experimental results show that the method can make the optimal decision in a real environment.