Grey Wolf-Based Task Scheduling in Vehicular Fog Computing Systems

Vehicular fog computing (VFC) can be considered an important alternative to address the existing challenges in intelligent transportation systems (ITS). The main purpose of VFC is to perform computational tasks through various vehicles. At present, VFCs include powerful computing resources that bring the computational resources nearer to the requesting devices. This paper presents a new algorithm based on a meta-heuristic optimization method for task scheduling problem in VFC. The task scheduling in VFC is formulated as a multi-objective optimization problem, which aims to reduce makespan and monetary costs. The proposed method utilizes grey wolf optimization (GWO) and assigns different priorities to static and dynamic fog nodes. Dynamic fog nodes represent the parked or moving vehicles and static fog nodes show the stationary servers. Afterward, the tasks that require the most processing resources are chosen and allocated to fog nodes. The GWO-based method is extensively evaluated in more detail. Furthermore, the effectiveness of various parameters in the GWO algorithm is analyzed. We also assess the proposed algorithm on real applications and random data. The outcomes of our experiments confirm that, in comparison to previous works, our algorithm is capable of offering the lowest monetary cost.

Keywords:

Task scheduling , Vehicular fog computing , Grey wolf optimization