Enhancing MapReduce Functionality for Optimizing Workloads on Data Centers?

In cloud computing environment, data centers are used to provide the services to storage computation. Most of the applications storing the data in data centers. Now a day’s even terabytes of data are supposed to be stored in data centers of cloud. The input datasets are geographically distributed on data centers. In many of the real applications, the data centers need to handle more requests. In order to handle more requests by the data centers, it uses more resources. So to reduce the resources used by the data centers, we designed a framework which is using the agents with MapReduce functionality. The MapReduce mechanism is commonly used for processing large datasets. In this paper, we analyse the possible ways of executing jobs and used to determine the scheduling of job sequences with respect to the execution time and monetary cost by the MapReduce functionality. Our evaluation shows that using MapReduce functionality with agents improves the processing time and cost of geographical distribution of datasets across data centers.