Fair Resource Allocation Based on Deep Reinforcement Learning in Fog Networks

Abstract

As the terminal devices grow explosively, the resource in a fog network may not satisfy all the requirement of them. Thus scheduling the resource reasonably becomes a huge challenge in the future 5G network. In the paper, we propose a fair resource allocation algorithm based on deep reinforcement learning, which makes full use of the computational resource in a fog network. The goal of the algorithm is to complete processing the tasks fairly for all the user nodes (UNs). The fog nodes (FNs) are expected to assign their central processing unit (CPU) cores to process offloading tasks reasonably. We apply the Deep Q-Learning Network (DQN) to solve the problem of resource scheduling. Firstly, we establish an evaluation model of a priority to set the priority for the offloading tasks, which is related to the reward in the reinforcement learning. Secondly, the model of reinforcement learning is built by taking the situation of UNs and resource allocation scheme as the state of environment and the action of the agent, respectively. Subsequently, a loss function is analysed to update the parameters of a deep neural network. Finally, numerical simulations demonstrate the feasibility and effectiveness of our proposed algorithm.

This work is supported in part by the National Natural Science Foundation of China (No. 61871370, No. 61601122 and No. 61773266), the Natural Science Foundation of Shanghai, China (No. 18ZR1437500 and No. 19ZR1423100), the Hundred Talent Program of Chinese Academy of Sciences (No. Y86BRA1001), the Fundamental Research Funds for the Central Universities (No. 2242019K40188), the Key Research & Development Plan of Jiangsu Province (No. BE2018108), and the Postdoctoral Science Foundation of China (No. 2019M651476).