Robust Resource Allocation Using Edge Computing for Vehicle to Infrastructure (V2I) Networks

TL;DR

This paper proposes a dynamic, uncertainty-aware resource allocation method for Vehicle-to-Infrastructure networks to enhance robustness and reduce service miss rates during peak demand or emergencies.

Contribution

It introduces a novel resource allocation approach that accounts for request uncertainty in federated edge environments for V2I systems.

Findings

Reduces service miss rate by up to 45% in simulations.

Improves system robustness during peak hours and disasters.

Enhances real-time response for autonomous vehicle safety.

Abstract

Development of autonomous and self-driving vehicles requires agile and reliable services to manage hazardous road situations. Vehicular Network is the medium that can provide high-quality services for self-driving vehicles. The majority of service requests in Vehicular Networks are delay intolerant (e.g., hazard alerts, lane change warning) and require immediate service. Therefore, Vehicular Networks, and particularly, Vehicle-to-Infrastructure (V2I) systems must provide a consistent real-time response to autonomous vehicles. During peak hours or disasters, when a surge of requests arrives at a Base Station, it is challenging for the V2I system to maintain its performance, which can lead to hazardous consequences. Hence, the goal of this research is to develop a V2I system that is robust against uncertain request arrivals. To achieve this goal, we propose to dynamically allocate service requests among Base Stations. We develop an uncertainty-aware resource allocation method for the federated environment that assigns arriving requests to a Base Station so that the likelihood of completing it on-time is maximized. We evaluate the system under various workload conditions and oversubscription levels. Simulation results show that edge federation can improve robustness of the V2I system by reducing the overall service miss rate by up to 45%.