Cost-optimal V2X Service Placement in Distributed Cloud/Edge Environment

TL;DR

This paper addresses the challenge of cost-efficient V2X service placement in distributed cloud/edge environments, proposing a model and heuristic algorithm to meet strict latency and QoS requirements effectively.

Contribution

It formulates the CO-VSP problem and introduces the DA-VSP heuristic algorithm, advancing solutions for latency-aware, cost-optimized V2X service deployment.

Findings

Both models and algorithms ensure QoS requirements are met.

The approach demonstrates a trade-off between latency and deployment cost.

Simulation results validate the effectiveness of the proposed methods.

Abstract

Deploying V2X services has become a challenging task. This is mainly due to the fact that such services have strict latency requirements. To meet these requirements, one potential solution is adopting mobile edge computing (MEC). However, this presents new challenges including how to find a cost efficient placement that meets other requirements such as latency. In this work, the problem of cost-optimal V2X service placement (CO-VSP) in a distributed cloud/edge environment is formulated. Additionally, a cost-focused delay-aware V2X service placement (DA-VSP) heuristic algorithm is proposed. Simulation results show that both CO-VSP model and DA-VSP algorithm guarantee the QoS requirements of all such services and illustrates the trade-off between latency and deployment cost.