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

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.