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

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.