Energy-Efficient Distributed Processing in Vehicular Cloud Architecture

TL;DR

This paper proposes an MILP-based model for optimizing resource allocation in vehicular cloud architectures, significantly reducing energy consumption compared to traditional cloud computing, especially for small demands.

Contribution

It introduces a novel MILP model that efficiently allocates processing tasks across vehicles, edge, and cloud to minimize power usage in vehicular networks.

Findings

Power savings of 70%-90% for small demands

20%-30% power savings for medium and large demands

Partial cloud usage due to capacity limits

Abstract

Facilitating the revolution for smarter cities, vehicles are getting smarter and equipped with more resources to go beyond transportation functionality. On-Board Units (OBU) are efficient computers inside vehicles that serve safety and non-safety based applications. However, much of these resources are underutilised. On the other hand, more users are relying now on cloud computing which is becoming costly and energy consuming. In this paper, we develop a Mixed Integer linear Programming (MILP) model that optimizes the allocation of processing demands in an architecture that encompasses the vehicles, edge and cloud computing with the objective of minimizing power consumption. The results show power savings of 70%-90% compared to conventional clouds for small demands. For medium and large demand sizes, the results show 20%-30% power saving as the cloud was used partially due to capacity limitations on the vehicular and edge nodes.