Fair Resource Allocation Based on Deep Reinforcement Learning in Fog Networks

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.