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We study the predictive power of autoregressive moving average models whenforecasting demand in two shared computational networks, PlanetLab and Tycoon.Demand in these networks is very volatile, and predictive techniques to planusage in advance can improve the performance obtained drastically.  Our key finding is that a random walk predictor performs best forone-step-ahead forecasts, whereas ARIMA(1,1,0) and adaptive exponentialsmoothing models perform better for two and three-step-ahead forecasts. A MonteCarlo bootstrap test is proposed to evaluate the continuous predictionperformance of different models with arbitrary confidence and statisticalsignificance levels. Although the prediction results differ between the Tycoonand PlanetLab networks, we observe very similar overall statistical properties,such as volatility dynamics.

Autoregressive Time Series Forecasting of Computational Demand