Predicting Machine Availabilities in Desktop Pools

This paper describes a study of predicting machine availabilities and user presence in a pool of desktop computers. The study is based on historical traces collected from 32 machines, and shows that robust prediction accuracy can be achieved even in this highly volatile environment. The employed methods include a multitude of classification methods known from data mining, such as Bayesian methods and Support Vector Machines. Further contribution is a time series framework used in the study which automates correlations search and attribute selection, and allows for easy reconfiguration and efficient prediction. The results illustrate the utility of prediction techniques in highly dynamic computing environments. Potential applications for proactive management of desktop pools are discussed.