Open Access

ARTICLE

Dynamic Multi-Objective Gannet Optimization (DMGO): An Adaptive Algorithm for Efficient Data Replication in Cloud Systems

P. William^1,2, Ved Prakash Mishra¹, Osamah Ibrahim Khalaf^3,*, Arvind Mukundan⁴, Yogeesh N⁵, Riya Karmakar⁶

1 School of Engineering, Architecture and Interior Design, Amity University Dubai, Dubai International Academic City, Dubai, P.O. Box 345019, United Arab Emirates
2 School of Engineering and Technology, Sanjivani University, Kopargaon, 423603, Maharashtra, India
3 Al-Nahrain Renewable Energy Research Center, Al-Nahrain University, Baghdad, 64040, Iraq
4 Department of Biomedical Engineering, Chennai Institute of Technology, Sarathy Nagar, Kundrathur, Malayambakkam, Chennai, 600069, Tamil Nadu, India
5 Department of Mathematics, Government First Grade College, Tumkur, 572102, Karnataka, India
6 Department of Mechanical Engineering, National Chung Cheng University, No. 168, Section 1, Daxue Rd, Minxiong Township, Chiayi County, 62102, Taiwan

* Corresponding Author: Osamah Ibrahim Khalaf. Email:

Computers, Materials & Continua 2025, 84(3), 5133-5156. https://doi.org/10.32604/cmc.2025.065840

Received 23 March 2025; Accepted 12 June 2025; Issue published 30 July 2025

Abstract

Cloud computing has become an essential technology for the management and processing of large datasets, offering scalability, high availability, and fault tolerance. However, optimizing data replication across multiple data centers poses a significant challenge, especially when balancing opposing goals such as latency, storage costs, energy consumption, and network efficiency. This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization (DMGO), designed to enhance data replication efficiency in cloud environments. Unlike traditional static replication systems, DMGO adapts dynamically to variations in network conditions, system demand, and resource availability. The approach utilizes multi-objective optimization approaches to efficiently balance data access latency, storage efficiency, and operational costs. DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency. Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms, achieving faster data access, lower storage overhead, reduced energy consumption, and improved scalability. The proposed methodology offers a robust and adaptable solution for modern cloud systems, ensuring efficient resource consumption while maintaining high performance.

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Keywords

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