Deep Reinforcement Learning-Based Task Scheduling in Heterogeneous MEC Networks
Ying Shang, Jinglei Li, Meng Qin, Qinghai Yang · 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) · 2022
In the era of Internet of Things (IoT), various computation-intensive applications emerge and bring great challenges to IoT devices with limited computation capability. Mobile edge computing (MEC) provides rich computing resources for IoT devices and improves applications’ execution efficiency. In this paper, we model applications as directed acyclic graphs (DAG) and target to minimize applications’ execution latency in heterogeneous MEC networks. To solve this problem, a Deep Q-Network (DQN)-based task scheduling (DQNTS) algorithm is proposed by utilizing deep reinforcement learning (DRL). By modeling the task scheduling process as a Markov decision process (MDP) and designing its critical elements, satisfying scheduling decisions are obtained. Simulation results show that the proposed algorithm achieves lower execution latency than the compared algorithms and it is adaptable to different MEC network environments.