Data Mining Approach for Modeling Risk Assessment in Computational Grid

Assessing Risk in a computational grid environment is an essential need for a user who runs applications from a remote machine on the grid, where resource sharing is the main concern. As Grid computing is the ultimate solution believed to meet the ever-expanding computational needs of organizations, analysis of the various possible risks to evaluate and develop solutions to resolve these risks is needed. For correctly predicting the risk environment, we made a comparative analysis of various machine learning modeling methods on a dataset of risk factors. First we conducted an online survey with international experts about the various risk factors associated with grid computing. Second we assigned numerical ranges to each risk factor based on a generic grid environment. We utilized data mining tools to pick the contributing attributes that improve the quality of the risk assessment prediction process. The empirical results illustrate that the proposed framework is able to provide risk assessment with a good accuracy.