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Open Access

ARTICLE

MDMOSA: Multi-Objective-Oriented Dwarf Mongoose Optimization for Cloud Task Scheduling

Olanrewaju Lawrence Abraham1,2,*, Md Asri Ngadi1, Johan Bin Mohamad Sharif1, Mohd Kufaisal Mohd Sidik1
1 Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor Bahru, 81310, Malaysia
2 Information Technology Services Department, Gateway (ICT) Polytechnic Saapade, Remo North, 121116, Nigeria
* Corresponding Author: Olanrewaju Lawrence Abraham. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.072279

Received 23 August 2025; Accepted 24 September 2025; Published online 23 December 2025

Abstract

Task scheduling in cloud computing is a multi-objective optimization problem, often involving conflicting objectives such as minimizing execution time, reducing operational cost, and maximizing resource utilization. However, traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems. To address this limitation, we introduce MDMOSA (Multi-objective Dwarf Mongoose Optimization with Simulated Annealing), a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service (IaaS) cloud environments. MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization (DMO) with the exploitation strengths of Simulated Annealing (SA), achieving a balanced search process. The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization. We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs (GoCJ) dataset within the CloudSim environment. Comparative analysis against benchmarked algorithms such as SMOACO, MOTSGWO, and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency, cost-effectiveness, and scalability. These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.

Keywords

Cloud computing; multi-objective; task scheduling; dwarf mongoose optimization; metaheuristic

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