An Evaluation of Bio-Inspired Resource Allocation Methods for Vehicular Edge Computing

Abstract

Researchers in vehicular edge computing are witnessing a continuous search for a solution to the problem of how to best allocate computational resources to fulfill service requests from road vehicles efficiently. This problem combines several of the most difficult challenges associated with intelligent transportation systems (ITSs), such as limited computational resources, dynamic vehicular network topology, high vehicle mobility, and long task execution times. These challenges represent significant barriers to the success of ITSs, severely impacting user experience and use of the service. Among alternatives, bio-inspired algorithms have been used to support the complex decision-making associated with resource optimization due to their perceived success in simulating various natural behaviors and dealing with complex environments. However, to our knowledge, a comprehensive demonstration of their suitability and performance was never made when faced with the mentioned challenges. To fill this gap, we comprehensively investigate how the most prominent bio-inspired algorithms perform in challenging scenarios in vehicular edge computing, and compare them with other widely adopted alternatives. Our results show that bio-inspired algorithms are both suitable and superior in efficiency, fulfilling a higher number of tasks.