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