Neuro-fuzzy risk prediction model for computational grids

Abstract

Prediction of risk assessment is demanding because it is one of the most important contributory factors towards grid computing. Hence, researchers were motivated for developing and deploying grids on diverse computers, which is responsible for spreading resources across administrative domains so that resource sharing becomes effective. Risk assessment in grid computing can analyses possible risks, that is, the risk of growing computational requirements of an organization. Thus, risk assessment helps in determining these risks. In this, we present an adaptive neuro-fuzzy inference system that can provide an insight of predicting the risk environment. The main goal of this paper is to obtain empirical results with an illustration of high performance and accurate results. We used data mining tools to determine the contributing attributes so that we can obtain the risk prediction accurately.