Optimized Data Replication in Cloud Using Hybrid Optimization Approach

ABSTRACT Cloud computing (CC), in contrast to traditional high‐performance computing environments, is a group of imaginary and networked resources of computing that are controlled by one unified maximum‐performance computing power. Here, this work aims to develop a novel data replication method in the cloud. The data replication is carried out with a new multi‐objective technique that considers constraints like cost, the distance between data centers, trust, and risk. Moreover, for optimal data replication, a new hybrid algorithm termed poor rich strategy assisted grasshopper optimization (PRS‐GO) is introduced. To increase the accessibility of the system, the data used continuously should be duplicated in various areas. A minimal mean value of 0.66 is gained with the PRS‐GO scheme, whereas Particle Swarm Optimization‐Tabu Search (PSO + TS), Receding Horizon Control (RHC), Sun Flower Optimization (SFO), Cat Mouse‐Based Optimization (CMBO), Hunger Games Search Optimization (HGSO), Seagull Optimization (SGO), Poor And Rich Optimization (PRO), and Grasshopper Optimization Algorithm (GOA) have got a high mean value of 0.722, 0.71, 0.71, 0.71, 0.7, 0.7, 0.7, and 0.69.