Grey Wolf-Based Task Scheduling in Vehicular Fog Computing Systems

TL;DR

This paper introduces a grey wolf optimization-based algorithm for task scheduling in vehicular fog computing, effectively reducing costs and balancing static and dynamic fog nodes in intelligent transportation systems.

Contribution

It presents a novel GWO-based multi-objective scheduling algorithm tailored for VFC, optimizing makespan and cost with priority assignment to different fog node types.

Findings

Achieves lower monetary costs compared to previous methods.

Effectively balances static and dynamic fog nodes.

Validated on real and random data sets.

Abstract

Vehicular fog computing (VFC) can be considered as 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 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 cost. The proposed method utilizes the grey wolf optimization (GWO) and assigns the 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. Afterwards, the tasks that require the most processing resources are chosen and allocated to fog nodes. The GWO-based method is extensively evaluated in more details. Furthermore, the effectiveness of various parameters in GWO algorithm is analyzed. We also assess the proposed algorithm on real application 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.