MINIMIZING DATA TRANSFER COST THROUGH ANT COLONY OPTIMIZATION IN THE CLOUD

The booming volume of data generated by contemporary business users and consumers have created enormous data storage and management challenges. Cloud computing provides a way to enable massive amounts of data to work together as data-intensive services. Many users are moving their data to online storage clouds, where data are stored based on the pay-as-you-go model. The cost and access response time of data sets influence the quality of the service that requires the data sets. There are many cost-effective approaches have been developed for achieving minimum cost benchmark. However, it may not be sufficient, if large-scale applications have to run in a more distributed manner. In this project, for incorporating data transfer cost into minimum cost benchmark, an effective Ant Colony Optimization (ACO) algorithm with Appriori algorithm has been proposed. ACO reduces the delay in accessing & transferring the data sets which paves the way to achieve a minimum cost benchmark. By using appriori algorithm, duplication of data sets after restarting can be avoided in case of any crashes in the cloud. Appriori algorithm is also used for retrieving frequent data items easily so that the cost for storing the infrequent large datasets can be reduced. This will drastically reduce the cost of the datasets storage along with the reduced cost of data transfer through ACO when there is a need of transferring data from one node into another node of the same or different cloud.