Abstract
Conventional evaluations of parallel job schedulers are based on simulating the outcome of using a new scheduler on an existing workload, as recorded in a log file. In order to check the scheduler’s performance under diverse conditions, crude manipulations of the whole log are used. We suggest instead to perform a high-resolution analysis of the natural variability in conditions that occurs within each log. Specifically, we use a heatmap of jobs in the log, where the X axis is the load experienced by each job, and the Y axis is the job’s performance. Such heatmaps show that the conventional reporting of average performance vs. average load is highly oversimplified. Using the heatmaps, we can see the joint distribution of performance and load, and use this to characterize and understand the system performance as recorded in the different logs. The same methodology can be applied to simulation results, enabling a better appreciation of different schedulers, and better comparisons between them.
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Krakov, D., Feitelson, D.G. (2013). High-Resolution Analysis of Parallel Job Workloads. In: Cirne, W., Desai, N., Frachtenberg, E., Schwiegelshohn, U. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 2012. Lecture Notes in Computer Science, vol 7698. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35867-8_10
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DOI: https://doi.org/10.1007/978-3-642-35867-8_10
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