Most IT data centers have evolved with the changing business needs, and, as a result, need to support a myriad of applications running across distributed servers in a variety of LANs, WANs, as well as across the Internet. IDC estimates that IT data centers spend up to 25% of their IT budget in management and support in essence to address their capacity and performance needs. Virtualization technology allows companies to consolidate applications spread over a large number of servers and disk drives to fewer servers and more compact storage farms. If done correctly, the virtualization solution can provide significant savings in deployment costs (capital expenditure), diminish complexity, and greatly reduce management and support costs. The success of a virtualization solution invariably depends upon the proper capacity planning and performance management of IT systems. After all, the resulting consolidated solution not only should support all the functionality of the parent system, but it should also provide acceptable response times and throughput, as well as provide scalability for future enhancements and expansions. The accurate capacity planning and performance management can be difficult and challenging. Due to the complexity of current IT infrastructures, the traditional approach of using elementary queuing models tends to fail, while usual simulation models are slow and require numerous system details to produce meaningful estimates. There is a definite need for an accurate modeling solution that can easily tackle arbitrary server configurations, and provide accurate performance results for arbitrary consolidation scenarios, including “what if” configuration planning before deploying a virtualization solution.