Data Replication Strategies with Load Balancing and Data Migration in Cloud Data Center

Abstract

The evolution of Information Systems implies new applications and the need to migrate the data from a previous application to a new one. At the same time, some organizations may need to replicate data from one technology to another one, in order to have backup systems and have a flexible load balanced strategies. The maximal uniform distribution of the load across closer and number of simpler nodes can help managing and providing the big data and large workloads which are more easy to handle. The ultimate goal is to balance the load through cloud and make internet less cloud defendant by having data available closer to the user end. One of the most challenging steps required to deploy an application infrastructure in the cloud involves the physics of moving data into and out of the cloud. Amazon Web Services (AWS) provides a number of services for moving data, and each solution offers various levels of speed, security, cost, and performance. This stems from the fact that almost all the typical distributed storage systems only provide data-amount-oriented balancing mechanisms without considering the different access load of data. To eliminate the system bottlenecks and optimize the resource utilization, there is a demand for such distributed storage systems to employ a workload balancing and adaptive resource management framework. We propose a framework of Enhanced replication scheduling algorithm which balances the replicated data to be balanced and to handle the overload data integration by data migration concept which gives more data efficiency and improved performance during migration of replicated data. For handling of data migration, we propose Ant Colony Algorithm which gives a safe data migration from one end to the other. This will improve the efficiency, Cost and takes less duration for the data to migrated and to be equally balanced.