Modelling Risk Assessment in a Computational Grid using the Data Mining Approach

Risk assessment in Grid computing is an important issue, because a Grid is a shared environment with diverse resources spread across several administrative domains, serving to meet the ever-expanding computational needs of organizations. Therefore, by assessing risk in Grid computing, it is possible to analyse potential risks regarding an organization's growing consumption of computational resources, and therefore this study seeks to improve organizational computation effectiveness. Firstly, it has navigated existing literature, and has explored risk factors that threaten Grid security. After preparing the risk factors dataset, the study utilized data mining tools to pick contributing attributes that improve the quality of the risk assessment prediction process. This paper has concluded that data mining tools can provide further steps in building a risk assessment model in a Grid environment with good accuracy, according to the obtained empirical results.