Dependency-Aware Vehicular Task Scheduling Policy for Tracking Service VEC Networks

In this paper, we study a tracking service vehicular edge computing (VEC) network that provides computation offloading service for Intelligent vehicles, where computational tasks with different urgency and dependency are required to be completed efficiently within strict time constraints. We consider the actual scenario where the environmental parameters fluctuate randomly and their distributions are unknown, thus, a long-term scheduling policy optimization problem needs to be solved. For this motivation, we first define a queueing criterion to sort the subtasks into a scheduling queue, and then model a specific Markov decision process (MDP) according to the scheduling queue. Furthermore, we propose our vehicular task scheduling policy optimizing (VTSPO) algorithm based on the most advanced policy-based deep reinforcement learning (DRL). The experimental results compared with known value-based DRL algorithms verify the advantages of the proposed VTSPO algorithm.