Workload resampling for performance evaluation of parallel job schedulers

Evaluating the performance of a computer system is based on using representative workloads. Common practice is to either use real workload traces to drive simulations or use statistical workload models that are based on such traces. Such models allow various workload attributes to be manipulated, thus providing desirable flexibility, but may lose details of the workload's internal structure. To overcome this, we suggest to combine the benefits of real traces and flexible modeling. Focusing on the problem of evaluating the performance of parallel job schedulers, we partition the trace of submitted jobs into independent subtraces representing different users and then recombine them in various ways, while maintaining features such as long-range dependence and the daily and weekly cycles of activity. This facilitates the creation of longer workload traces that enable longer simulations, the creation of multiple statistically similar workloads that can be used to gauge confidence intervals, the creation of workloads with different load levels, and increasing the frequency of specific events like large surges of activity.