Abstract: The rapid advancement of the Internet and Internet of Things (IoT) technologies has significantly increased the demand for scalable and efficient cloud computing solutions. Task scheduling, a critical aspect of cloud computing, directly impacts system performance by influencing resource utilization, execution time, and operational costs. However, scheduling tasks in large-scale, dynamic cloud environments remains an NP-hard problem, with existing metaheuristic methods often struggling with scalability, convergence, and adaptability. This study proposes a novel task scheduling approach based on the dwarf mongoose optimization (DMO) algorithm. To assess its effectiveness, we conduct two experimental scenarios. The results demonstrate that, compared with existing algorithms, the proposed DMO algorithm offers faster convergence and higher accuracy in identifying optimal task scheduling solutions, particularly under large-scale task loads. We evaluated the method using the Google Cloud Jobs (GoCJ) dataset, and the findings confirm that DMO outperforms prior state-of-the-art techniques in terms of reducing makespan.

Olanrewaju Lawrence Abraham, Md Asri Ngadi, Johan Bin Mohamad Sharif, Mohd Kufaisal Mohd Sidik and Ogunyinka Taiwo Kolawole. “Task Scheduling in Cloud Computing Environment Based on Dwarf Mongoose Optimization”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161273

@article{Abraham2025,
title = {Task Scheduling in Cloud Computing Environment Based on Dwarf Mongoose Optimization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161273},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161273},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Olanrewaju Lawrence Abraham and Md Asri Ngadi and Johan Bin Mohamad Sharif and Mohd Kufaisal Mohd Sidik and Ogunyinka Taiwo Kolawole}
}