A Parallel Workload Model and its Implications for Maui Scheduling Policies

Authors:
Zhuo Liu;Aihua Liang;Limin Xiao
Affiliations:
-;-;-
Venue:
ICCMS '10 Proceedings of the 2010 Second International Conference on Computer Modeling and Simulation - Volume 02
Year:
2010

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Multi-objective cooperative coevolution of artificial neural networks (multi-objective cooperative networks)

Neural Networks

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Abstract

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.