Meeting Deadlines in Motion: Deep RL for Real-Time Task Offloading in Vehicular Edge Networks

TL;DR

This paper explores the use of deep reinforcement learning to optimize real-time task offloading in vehicular edge networks, significantly reducing latency and dropped tasks compared to traditional methods.

Contribution

It introduces a novel application of DRL models like DQN and PPO for dynamic task offloading in VEC, outperforming traditional PSO approaches.

Findings

DQN reduces execution time by 99.2%

DQN decreases dropped tasks by 2.5%

DQN achieves 18.6% lower E2E latency

Abstract

Vehicular Mobile Edge Computing (VEC) drives the future by enabling low-latency, high-efficiency data processing at the very edge of vehicular networks. This drives innovation in key areas such as autonomous driving, intelligent transportation systems, and real-time analytics. Despite its potential, VEC faces significant challenges, particularly in adhering to strict task offloading deadlines, as vehicles remain within the coverage area of Roadside Units (RSUs) for only brief periods. To tackle this challenge, this paper evaluates the performance boundaries of task processing by initially establishing a theoretical limit using Particle Swarm Optimization (PSO) in a static environment. To address more dynamic and practical scenarios, PSO, Deep Q-Network (DQN), and Proximal Policy Optimization (PPO) models are implemented in an online setting. The objective is to minimize dropped tasks and reduce end-to-end (E2E) latency, covering both communication and computation delays. Experimental results demonstrate that the DQN model considerably surpasses the dynamic PSO approach, achieving a 99.2% reduction in execution time. Furthermore, It leads to a reduction in dropped tasks by 2.5% relative to dynamic PSO and achieves 18.6\% lower E2E latency, highlighting the effectiveness of Deep Reinforcement Learning (DRL) in enabling scalable and efficient task management for VEC systems.