Power Minimization in Vehicular Cloud Architecture

TL;DR

This paper proposes a vehicular cloud architecture utilizing fixed edge nodes and central cloud, optimizing resource allocation to significantly reduce power consumption in smart city vehicular networks.

Contribution

It introduces a novel vehicular cloud architecture and develops a mixed integer linear programming model for power-efficient resource allocation, along with a near-optimal heuristic for real-time use.

Findings

Power savings up to 84% compared to traditional cloud processing.

Development of a MILP model for optimal resource allocation.

Design of a heuristic with near-MILP performance for real-time demands.

Abstract

Modern vehicles equipped with on-board units (OBU) are playing an essential role in the smart city revolution. The vehicular processing resources, however, are not used to their fullest potential. The concept of vehicular clouds is proposed to exploit the underutilized vehicular resources to supplement cloud computing services to relieve the burden on cloud data centers and improve quality of service. In this paper we introduce a vehicular cloud architecture supported by fixed edge computing nodes and the central cloud. A mixed integer linear programming (MLP) model is developed to optimize the allocation of the computing demands in the distributed architecture while minimizing power consumption. The results show power savings as high as 84% over processing in the conventional cloud. A heuristic with performance approaching that of the MILP model is developed to allocate computing demands in real time.