Time-Driven Scheduling Based on Reinforcement Learning for Reasoning Tasks in Vehicle Edge Computing

Abstract

Significant challenges for reasoning tasks scheduling remain, including the selection of an optimal tasks-servers solution from the possible numerous combinations, due to the heterogeneous resources in edge environments and the complicated data dependencies in reasoning tasks. In this study, a time-driven scheduling strategy based on reinforcement learning (RL) for reasoning tasks in vehicle edge computing is designed. Firstly, the reasoning process of vehicle applications is abstracted as a model based on directed acyclic graphs. Secondly, the execution order of subtasks is defined according to the priority evaluation method. Finally, the optimal tasks-servers scheduling solution is chosen by Deep Q-learning (DQN). The extensive simulation experiments show that the proposed scheduling strategy can effectively reduce the completion delay of reasoning tasks. It performs better in algorithm convergence and runtime compared with the classic algorithms.