Jaccard Index based availability prediction in enterprise grids

Enterprise Grid enables sharing and aggregation of a set of computing or storage resources connected by enterprise network, but the availability of the resources in this environment varies widely. Thus accurate prediction of the availability of these resources can significantly improve the performance of executing compute-intensive complex scientific and business applications in enterprise Grid environment by avoiding possible runtime failures. In this paper, we propose a Jaccard Index based prediction approach utilizing lazy learning algorithm that searches for a best match of a sequence pattern in the historical data in order to predict the availability of a particular machine in the system. We compare it against three other well known availability prediction techniques using simulation based study. The experimental results show that our Jaccard Index based prediction approach achieves better prediction accuracy with reduced computational complexity when compared to other similar techniques