A Novel Intelligent Approach for Dynamic Data Replication in Cloud Environment

In recent years, cloud computing research, specifically data replication techniques and their applications, has been growing. If the replicas number is raised put in multiple positions, it will be expensive to maintain usability, performance stability of application systems. this paper, two bio- inspired algorithms were proposed improve both selection placement environment. The suggested for dynamic are multi-objective particle swarm optimization (MO-PSO) ant colony (MO-ACO). first algorithm, i.e ., MO-PSO, employed obtain best selected replica depend on most frequent one. However, second MO-ACO, shortest distance, availability. A simulation strategy was carried out using CloudSim. Each center (DC) includes hosts with set virtual machines (VMs). order determined at random from a thousand cloudlets. All files randomly distributed architecture. evaluated against several approaches including: Adaptive Replica Dynamic Strategy (ARDS), Enhance Fast Spread (EFS), Genetic Algorithm (GA), Selection Placement (RSP), Popular File Replication First (PFRF), Cost-aware Re-replication Re-balancing (DCR2S). results prove that MOPSO gives improved compared other algorithms. Additionally, MOACO realizes higher availability, lower cost, less bandwidth consumption