Predictability of process resource usage: a measurement-based study on UNIX

A statistical approach is developed for predicting the CPU time, the file I/O, and the memory requirements of a program at the beginning of its life, given the identity of the program. Initially, statistical clustering is used to identify high-density regions of process resource usage. The identified regions form the states for building a state-transition model to characterize the resource usage of each program in its past executions. The prediction scheme uses the knowledge of the program´s resource usage in its last execution together with its state-transition model to predict the resource usage in its next execution. The prediction scheme is shown to work using process resource-usage data collected from a VAX 11/780 running 4.3 BSD Unix. The results show that the predicted values correlate strongly with the actual; the coefficient of correlation between the predicted and actual values for CPU time is 0.84. The errors in prediction are mostly small and are heavily skewed toward small values