Efficient Framework for Resource allocation and Monitoring Approach for Data centers and Virtual Environment

In our thesis Virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of "skewness" to measure the unevenness in the multidimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server resources. Resource allocation process must be scalable both in the number of machines in the cloud and the number of sites that the cloud hosts. Green scheduling can determine which server to be in running state. Control a different set of technique is used to predict non-stationary workloads of the system. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.