Efficient Data Replication Management in the Cloud Using a Hybrid Meta Heuristics Optimization Model

A powerful paradigm called cloud computing, which shares information across the Internet, quickly serves the demands of companies and individuals. A data replication approach for data nodes is essential to maximizing load balancing and minimizing access time, ensuring data availability in large-scale cloud storage systems. This paper proposes a data replication management system in the cloud using a hybrid optimization strategy called adaptive pelican with butterfly optimization algorithm (APBOA), which combines pelican optimization algorithm (POA) and butterfly optimization algorithm (BOA) to select the optimal locations for replications and which files to replicate. The hybrid optimization balances exploration and exploitation to achieve superior performance, where the diverse cloud regions and storage options were explored using the pelican optimization and data placement, and cost efficiency and data placement replication frequency are optimized using butterfly optimization. The outcomes demonstrated that our method is effective and outperforms conventional approaches by achieving a less energy consumption of 1.15. So, the hybrid approach is cost-effective, more efficient, and dynamically adaptable to changing conditions in the cloud.