A simulator for data-intensive job scheduling

Despite the fact that size-based schedulers can give excellent results in terms of both
average response times and fairness, data-intensive computing execution engines generally
do not employ size-based schedulers, mainly because of the fact that job size is
not known a priori.
In this work, we perform a simulation-based analysis of the performance of sizebased
schedulers when they are employed with the workload of typical data-intensive
schedules and with approximated size estimations. We show results that are very
promising: even when size estimation is very imprecise, response times of size-based
schedulers can be definitely smaller than those of simple scheduling techniques such
as processor sharing or FIFO.