To perform a schedulability test, scheduling analysis relies on a known worst-case execution time (WCET). This value may be difficult to compute and may be overly pessimistic. This paper offers an alternative analysis based on estimating a WCET from test data to within a specific level of probabilistic confidence. A method is presented for calculating an estimate given statistical assumptions. The implications of the level of confidence on the likelihood of schedulability are also presented.

Estimating computation times using analysis techniques is always safe but is becoming prohibitively complex or pessimistic with modern processors. The only alternative approach is to use measurement, but this has the significant disadvantage of optimism - the largest value seen during testing may not be the largest experienced during deployment. In this paper, we subject data obtained from measurement to statistical analysis using the techniques of extreme value estimation. A simple case study is described and the approach is illustrated via this study which focuses on the superscalar technique of branch prediction. The approach is applicable to all forms of hardware-induced temporal variability.