Knowledge-Guided Attention-Inspired Learning for Task Offloading in Vehicle Edge Computing

TL;DR

This paper introduces KATO, a learning-based method that uses attention mechanisms to efficiently offload tasks from vehicles to RSUs in vehicle edge computing, reducing delay and computational costs.

Contribution

The paper presents a novel attention-inspired learning approach for task offloading in vehicle edge computing, improving efficiency and solution quality over existing methods.

Findings

KATO achieves near-optimal task offloading performance.

It significantly reduces computational overhead.

The method generalizes well across different network configurations.

Abstract

Vehicle edge computing (VEC) brings abundant computing resources close to vehicles by deploying them at roadside units (RSUs) or base stations, thereby enabling diverse computation-intensive and delay sensitive applications. Existing task offloading strategies are often computationally expensive to execute or generate suboptimal solutions. In this paper, we propose a novel learning-based approach, Knowledge-guided Attention-inspired Task Offloading (KATO), designed to efficiently offload tasks from moving vehicles to nearby RSUs. KATO integrates an attention-inspired encoder-decoder model for selecting a subset of RSUs that can reduce overall task processing time, along with an efficient iterative algorithm for computing optimal task allocation among the selected RSUs. Simulation results demonstrate that KATO achieves optimal or near-optimal performance with significantly lower computational overhead and generalizes well across networks of varying sizes and configurations.