A Bee Colony-Based Algorithm for Task Offloading in Vehicular Edge Computing

Abstract

Complex vehicular applications, such as automatic driving and augmented reality are delay sensitive and require massive computational resources. Despite being more connected and smarter, vehicles still cannot appropriately meet the demands of these applications. By allowing neighboring vehicles and edge servers coupled to base stations to share their available computing resources, vehicular edge computing systems help to handle these applications. Then, vehicles can use the task offloading technique by sending application tasks to be executed remotely and receiving the processing results later. Although this technique aims to reduce application execution time, performing it in vehicular scenarios is challenging. In such scenarios, network nodes vary their computing and energy loads and move quickly, causing frequent disconnections and failures. Thus, we propose an algorithm called Bee colony-based Task offloading in Vehicular edge computing (BTV) to reliably reduce the execution time of applications in vehicular edge computing systems. The BTV algorithm provides task scheduling solutions to different servers in a feasible time, using several contextual parameters and wireless access in vehicular environments and fifth-generation networks. Experimental results show that our solution can reduce the average execution time of applications by up to 74.4% and with up to 0.0% of failures, outperforming other existing solutions.