What question did this study set out to answer?

This research aims to optimize workflow task scheduling in cloud-fog computing systems using a hybrid algorithm.

June 12, 2026

Optimizing Workflow Scheduling in Cloud‐Fog Computing: An Improved Approach

Key Points

This research aims to optimize workflow task scheduling in cloud-fog computing systems using a hybrid algorithm.
Developed the HABSO algorithm by integrating the artificial bee colony (ABC) and butterfly swarm optimization (BSO) algorithms.
Conducted simulations to evaluate the performance of HABSO against classical metaheuristic algorithms.
Performed sensitivity analysis, convergence behavior assessment, and statistical evaluations.
HABSO achieved an average reduction of 14.35% in total execution time compared to ABC and 18.59% compared to BSO.
Energy savings of 14.33% were obtained with the ABC algorithm and 11.62% with BSO, among others.
The hybrid approach demonstrated effective performance across varying workloads and task dependencies.

Abstract

ABSTRACT Purpose Workflow task scheduling performs a vital role in optimizing performance in cloud‐fog computing systems, namely, the combination of centralized cloud servers and decentralized fog nodes to support Internet of Things (IoT) applications. Efficient scheduling guarantees the lowest delay, energy consumption, execution time, and cost. However, owing to the heterogeneous and dynamic characteristics of such environments, task scheduling has been recognized as an NP‐hard problem, in which finding the global optimum solution is computationally infeasible. Methodology To mitigate these problems, our proposed solution is the hybridization of the artificial bee colony (ABC) algorithm and the butterfly swarm optimization (BSO) algorithm as the HABSO algorithm for scheduling workflow tasks. The ABC algorithm, which is known for its global search capabilities, is used to explore possible solutions within the wide and complex space of cloud‐fog systems, whereas BSO aids in enhancing the local search capabilities and fine‐tuning of solutions to ensure rapid convergence and the best possible performance. This hybrid algorithm provides a good balance between scheduling objectives for the task to be finished, a factor that minimizes the total execution time (TET) and energy consumption (EC) of the computational resources. Findings The simulation results indicate the superiority of the proposed HABSO hybrid algorithm over classical state‐of‐the‐art metaheuristic algorithms, with average reductions of 14.35% (ABC), 18.59% (BSO), 14.02% (GWO), 16.26% (WOA), 13.41% (PSO‐WOA), and 12.01% (GWO‐MSOA) for the TET. Similarly, energy savings of 14.33%, 11.62%, 12.72%, 12.73%, 13.07%, and 8.85% were obtained for the ABC, BSO, GWO, WOA, PSO‐WOA, and GWO‐MSOA, respectively. Furthermore, sensitivity evaluation, convergence behaviour and statistical evaluations were conducted. Originality The proposed algorithm provides better performance at different workloads and task dependencies; therefore, it has the advantage of improving task scheduling for cloud‐fog computing systems.

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