A problem facing a several areas in the industry comprise of a rapid increase in data as well as how to deal with it efficiently. Most of the cases, the data involved is a useful resource and hence the problem that arise is how to extract a meaning from the data. Inherently, while the standard servers in a stock market have been used in the provision of data to the traders, new technologies that are developed in virtual environments let the traders to incorporate new features that will help them to detect the patterns along with predicting the direction of the stock market and more importantly, it yields clues for future data mining research.
This paper describes a virtualization project that is set to be used for trading stocks and comparing the performance of the virtual environment with the use of standard servers in Nasdaq stock market. Subsequently, the evaluation of the project is based on different types of virtualization mechanisms compared to standard physical server deployment.