MACH: Multi-Agent Coordination for RSU-centric Handovers

TL;DR

MACH is a decentralized approach for vehicular task handover that enhances QoS and load balancing by leveraging RSUs and real-time contextual data, improving efficiency and adaptability in vehicular computing.

Contribution

MACH introduces a decentralized, edge-based handover method for vehicular computing that considers real-time context to optimize task offloading and resource utilization.

Findings

Significantly improves handover efficiency and adaptability.

Reduces communication overhead in vehicular networks.

Balances computational loads among RSUs effectively.

Abstract

This paper introduces MACH, a novel approach for optimizing task handover in vehicular computing scenarios. To ensure fast and latency-aware placement of tasks, the decision-making -- where and when should tasks be offloaded -- is carried out decentralized at the Road Side Units (RSUs) who also execute the tasks. By shifting control to the network edge, MACH moves away from the traditional centralized or vehicle-based handover method. Still, it focuses on contextual factors, such as the current RSU load and vehicle trajectories. Thus, MACH improves the overall Quality of Service (QoS) while fairly balancing computational loads between RSUs. To evaluate the effectiveness of our approach, we develop a robust simulation environment composed of real-world traffic data, dynamic network conditions, and different infrastructure capacities. For scenarios that demand low latency and high reliability, our experimental results demonstrate how MACH significantly improves the adaptability and efficiency of vehicular computations. By decentralizing control to the network edge, MACH effectively reduces communication overhead and optimizes resource utilization, offering a robust framework for task handover management.