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Grey Wolf-Based Task Scheduling in Vehicular Fog Computing Systems

Maryam Taghizadeh, Mahmood Ahmadi

TL;DR

This work addresses task scheduling in vehicular fog computing (VFC) to simultaneously reduce completion time (makespan) and monetary cost. It introduces a multi-objective formulation solved via a Grey Wolf Optimization (GWO) algorithm that allocates tasks to static fog nodes first, then dynamic fog nodes, and finally cloud resources, using a weighted sum $F = \alpha \cdot Cost + \beta \cdot CT$ with $\alpha + \beta = 1$. The problem formulation includes $ET_i$, $PT_i$, and a binary assignment matrix $X_{ij}$, with objective-driven priority and normalization to compare heterogeneous resources. Experimental results on synthetic scenarios and a Montage scientific workflow show that the GWO-based approach achieves lower monetary cost than baselines, with dynamic fog nodes offering meaningful improvements in the cost-time trade-off. Overall, the method demonstrates the viability of GWO for scalable, edge-driven scheduling in hybrid cloud-fog vehicular networks, highlighting practical considerations for node reliability and mobility.

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.

Grey Wolf-Based Task Scheduling in Vehicular Fog Computing Systems

TL;DR

This work addresses task scheduling in vehicular fog computing (VFC) to simultaneously reduce completion time (makespan) and monetary cost. It introduces a multi-objective formulation solved via a Grey Wolf Optimization (GWO) algorithm that allocates tasks to static fog nodes first, then dynamic fog nodes, and finally cloud resources, using a weighted sum with . The problem formulation includes , , and a binary assignment matrix , with objective-driven priority and normalization to compare heterogeneous resources. Experimental results on synthetic scenarios and a Montage scientific workflow show that the GWO-based approach achieves lower monetary cost than baselines, with dynamic fog nodes offering meaningful improvements in the cost-time trade-off. Overall, the method demonstrates the viability of GWO for scalable, edge-driven scheduling in hybrid cloud-fog vehicular networks, highlighting practical considerations for node reliability and mobility.

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.

Paper Structure

This paper contains 18 sections, 26 equations, 9 figures, 19 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Machine learning-based methods
  4. Meta-heuristic-based methods
  5. System model
  6. System architecture
  7. Problem formulation
  8. Proposed method
  9. Grey wolf optimization (GWO) algorithm
  10. Task scheduling algorithm
  11. Time complexity
  12. Experimental results
  13. Implementation parameters
  14. GWO results
  15. Results and Comparison
  16. ...and 3 more sections

Figures (9)

  • Figure 1: System model
  • Figure 2: Pseudo code of grey wolf optimization (GWO)
  • Figure 3: Pseudo code of task scheduling algorithm
  • Figure 4: Effect of different values at iteration parameter in GWO algorithm
  • Figure 5: Effect of different values at a parameter in GWO algorithm
  • ...and 4 more figures