Modeling and Minimizing Latency in Three-tier V2X Networks

Abstract

Leveraging mobile cloud computing (MCC) and mobile edge computing (MEC) for offloading computational tasks is a promising approach to enabling delay-sensitive applications executing vehicles. Despite MCC and MEC's ability and complementary characteristics, most of the existing works on offloading focus on only either MCC or MEC. In this paper, we study their cooperation in a three-tier offloading model of a V2X network where a vehicle can offload computational tasks to cloud computing and MEC. Specifically, we investigate the optimal offloading probabilities of three offloading paths, including Vehicle-to- Infrastructure, Vehicle-to-Cloud, and Infrastructure-to-Cloud. Our contribution is twofold. First, we derive a mathematical model of task execution latency and a formulation to find an optimal solution for the minimum latency problem. Second, we propose an approximation algorithm based on the genetic algorithm toward the optimum. The experiment results show that by exploiting both MCC and MEC's complementary advantages, our proposed algorithm in the three-tier model can shorten the delay significantly compared to existing two-tier models. Depending on the traffic load and the number of Road Side Units, our proposal can reduce the delay by 93.75% on the average, and 99.9% in the best case.