A Customized Genetic Algorithm for SLA-Aware Service Provisioning in Infrastructure-Less Vehicular Cloud Networks

Abstract

Vehicular Ad-hoc Networks (VANETs) and in-vehicle networks offer complementary perspectives on Intelligent Transportation Systems (ITS), enabling communication between vehicles and within individual vehicles, respectively. While VANETs focus on vehicle-to-vehicle communication, the growing demand for dynamic resource sharing and data processing across a fleet of vehicles highlights the need for Vehicular Cloud Networks (VCNs). VCNs, despite their lack of fixed infrastructure and the continuous mobility of vehicles, provide a promising solution for improving resource management and data sharing, making them critical for achieving efficient Service Level Agreements (SLAs) in infrastructure-less environments. This article addresses these challenges by employing a hierarchical clustering technique and proposing a novel mathematical formulation for resource provisioning in infrastructure-less vehicular clouds. The formulation considers diverse criteria, including provider and requester mobility, data volume, and service delay tolerance, to ensure SLA adherence. A customized genetic algorithm is used to solve the maximization problem, incorporating a grouping mechanism for efficient problem solving. Simulations using the NS2 network simulator and the IBM CPLEX optimization tool validate the feasibility of the proposed approach and demonstrate its superior performance compared to the other methods.