A Parallel Workload Model and its Implications for Maui Scheduling Policies

We develop a workload model based on the logs of real workloads of the Linux cluster Atlas and a small IBM blue gene/L cluster at Lawrence Livermore National Laboratory (LLNL), and the 184-node IBM eServer pSeries 655/690 at the San Diego Supercomputer Center (SDSC). This model gives us insight into the performance of scheduling jobs on space-sharing parallel computers, provided by Maui. We find out that backfill queuing policies improve a lot of the system performance, and without reservation, the allocation policies do not have an obvious distance between each other.