Forecasting CPU availab ility in volunteer computing systems using a single prediction algorith m is insufficient due to the d iversity of the world -wide distributed resources. In this paper, we draw-up the main guidelines to develop an appropriate CPU availability prediction system fo r such computing infrastructures. To reduce solution time and to enhance precision, we use simp le pred iction techniques, precisely vector autoregressive models and a tendency-based technique. We propose a predictor construction process which automatically checks assumptions of vector autoregressive models in time series. Three different past analyses are performed. For a given volunteer resource, the proposed prediction system selects the appropriate predictor using the mult i-state based prediction technique. Then, it uses the selected predictor to forecast CPU availability indicators. We evaluated our predict ion system using real traces of more than 226000 hosts of Seti@ho me. We found that the proposed prediction system improves the prediction accuracy by around 24%.Index Terms-CPU availab ility prediction, predict ion system, mult ivariate time series, mult i-state based prediction, volunteer computing system. Automated Forecasting Approach M inimizing Prediction Errors of CPU 9 Availability in Distributed Computing Systems

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