A Bat Bio-inspired Mechanism for Resource Allocation in Vehicular Clouds

Abstract

The growth of vehicles in cities brings significant socioeconomic problems and new challenges. With that growth, the amount of information generated by vehicles and their devices also increments and can improve the network using Vehicular Ad Hoc Networks (VANET). VANETs make the communication between vehicles and infrastructures possible to exchange information and share resources. To assist VANETs, another concept called Vehicular Cloud Computing (VCC) brings the Cloud paradigms to this scenario. In this paper, we propose a Bat Bio-inspired Mechanism for Resource Allocation in Vehicular Clouds, called NAUTILUS. The algorithm uses the metaheuristic to optimize the search process for defining pseudo-optimal decision-making of the allocation process in a Vehicular Cloud. We also consider a fog-based paradigm to assist the proposed mechanism in the allocation process. We allocate the following resources from the vehicles: storage, memory, runtime, and processing. The NAUTILUS was compared to two other algorithms that use traditional search techniques: a Greedy approach and an Analytic Hierarchy Process (AHP) approach. In the comparison process, we evaluate the number of blocked, attended, and denied services. Simulations results show that the NAUTILUS presented better efficiency than Greedy and AHP approaches in all three performance aspects: blocking fewer, attending more, and denying fewer services.