An Enhanced Cloud Allocation Approach based on Metaheuristics Algorithms | ||||
| Labyrinth: Fayoum Journal of Science and Interdisciplinary Studies | ||||
| Volume 3, Issue 1, June 2025, Page 71-80 PDF (1.49 MB) | ||||
| Document Type: Original full papers (regular papers) | ||||
| DOI: 10.21608/ifjsis.2025.369477.1110 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Mohammed EM Shaaban1; Sayed Abdel Gaber2; Rasha M. Badry  3
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| 1Information Systems Department. | ||||
| 2Information Systems Department, Faculty of Computers and Artificial Intelligence Helwan University Helwan, Egypt | ||||
| 3Faculty of Computers and Artificial Intelligence, Fayoum University, Fayoum Egypt. | ||||
| Abstract | ||||
| In cloud computing environments, effective data placement is critical for optimizing system performance and resource utilization. This research introduces an innovative framework: Enhanced Cloud Data Placement Strategy Using Marine Predator Optimization. This algorithm leverages the Marine Predators Algorithm (MPA) -a nature-inspired metaheuristic inspired by marine predators' hunting behavior, balancing exploration and exploitation for efficient optimization-, to address theis challenge. The proposed framework leverages MPA’s exploration and exploitation capabilities to reduce data movement between data centers, and enhance resource allocation efficiency. Through simulation in a controlled environment using the CloudSim toolkit, we evaluated the performance of MPA in comparison with other state-of-the-art metaheuristic algorithms, including the Gaining Sharing Knowledge-based Algorithm (GSKA), War Strategy Optimization (WarSO), Generalized Whale Optimization Algorithm (GWO_WOA), and Success History Intelligent Optimizer (SHIO). Experimental results demonstrate that MPA outperforms these algorithms in terms of runtime, and overall resource utilization. Further tests, including scalability evaluations with increasing dataset sizes and data center numbers, revealed MPA’s robustness and adaptability for large-scale cloud infrastructures. The performance comparison indicates that applying MPA to solve the proposed problems consistently yields lower makespan and runtime, positioning it as a promising solution for dynamic and heterogeneous cloud environments. | ||||
| Keywords | ||||
| Data placement; Big Data; Cloud Computing; Metaheuristics; Marine Predators Algorithm | ||||
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