Highly Accurate Prediction of Jobs Runtime Classes

Supplying job schedulers with information on how long the jobs are expected to run enabled the development of the backfilling algorithms, which leverage this information to pack the jobs more efficiently and improve system utilization [1]. These algorithms, however, were designed for parallel systems, in which the jobs require many processors in order to execute, and processor fragmentation (idleness) is a big concern. In those environments the scheduler needs to know the actual runtimes of the jobs (use numeric predictions) to be able to optimize the schedule and improve performance [10]. Our work targets systems in which most jobs are serial, like server farms that are used for software testing. In those environments sophisticated scheduling algorithms are not required, and in order to improve performance it is enough to simply separate the short jobs from the long and assign them to different queues in the system [12]. This separation reduces the likelihood that short jobs will be delayed after long ones, improves the average turnaround times of the jobs and overall system throughput.