Applying Web service and Windows clustering for high volume risk analysis

We present the development of a distributed system to calculate the Value at Risk (VaR) measure when a large number of users are presented. A scalable architecture based on Windows clustering and Web services is proposed. In addition, we develop a load balancing algorithm to distribute the workload among the compute nodes in the Windows cluster. The experimental results show that our system can substantially speed up the VaR calculation. In addition, it offers a good scalability. This work provides an example of how to deploy a standard Web service and Windows clustering technology to offer a cost-effective and scalable solution for demanding financial applications in practice.