DRL-Based Edge Computing Model to Offload the FIFA World Cup Traffic

Abstract

In recent years, the volume of global video traffic has been increasing rapidly and it is considerably significant to offload the traffic during the process of video transmission and improve the experience of users. In this paper, we propose a novel traffic offloading strategy to provide a feasible and efficient reference for the following 2022 FIFA World Cup held in Qatar. At first, we present the system framework based on the Mobile Edge Computing (MEC) paradigm, which supports transferring the FIFA World Cup traffic to the mobile edge servers. Then, the Deep Reinforcement Learning (DRL) is used to provide the traffic scheduling method and minimize the scheduling time of application programs. Meanwhile, the task scheduling operation is regarded as the process of Markov decision, and the proximal policy optimization method is used to train the Deep Neural Network in the DRL. For the proposed traffic offloading strategy, we do the simulation based on two real datasets, and the experimental results show that it has smaller scheduling time, higher bandwidth utilization, and better experience of user than two baselines.

Decentralized Offloading Strategies Based on Reinforcement Learning for Multi-Access Edge Computing

“…Round-Robin is also a heuristic method. PPO [29] is a reinforcement learning-based method that uses the Proximal Policy Optimization (PPO) algorithm to train the joint offloading strategy for edge servers. Different from our method, PPO regards just the task set as the state for servers.…”