Edge Collaborative Task Scheduling and Resource Allocation Based on Deep Reinforcement Learning

With the development of the sixth generation mobile network (6G), the arrival of the Internet of Everything (IoE) is accelerating. An edge computing network is an important network architecture to realize the IoE. Yet, allocating limited computing resources on the edge nodes is a significant challenge. This paper proposes a collaborative task scheduling framework for the computational resource allocation and task scheduling problems in edge computing. The framework focuses on bandwidth allocation to tasks and the designation of target servers. The problem is described as a Markov decision process (MDP). To minimize the task execution delay and user cost and improve the task success rate, we propose a Deep Reinforcement Learning (DRL) based method. In addition, we explore the problem of the hierarchical hash rate of servers in the network. The simulation results show that our proposed DRL-based task scheduling algorithm outperforms the baseline algorithms in terms of task success rate and system energy consumption. The hierarchical settings of the server's hash rate also show significant benefits in terms of improved task success rate and energy savings.