Computer Science Department
School of Computer Science, Carnegie Mellon University


The Statistical Properties of Host Load

Peter A. Dinda, David R. O'Hallaron

July 1998

A version of this paper will appear in the Proceedings of the Fourth Workshop on Languages, Compilers, and Run-time Systems for Scalable Computers (LCR98).

Keywords: Load traces, load statistics, statistical analysis, time-series analysis, self-similarity, epochal behavior

Understanding how host load changes over time is instrumental in predicting the execution time of tasks or jobs, such as in dynamic load balancing and distributed soft real-time systems. To improve this understanding, we collected week-long, 1 Hz resolution Unix load average traces on 38 different machines including production and research cluster machines, compute servers, and desktop workstations Separate sets of traces were collected at two different times of the year. The traces capture all of the dynamic load information available to user-level programs on these machines. We present a detailed statistical analysis of these traces here, including summary statistics, distributions, and time series analysis results. Two significant new results are that load is self-similar and that it displays epochal behavior. All of the traces exhibit a high degree of self similarity with Hurst parameters ranging from .63 to .97, strongly biased toward the top of that range. The traces also display epochal behavior in that the local frequency content of the load signal remains quite stable for long periods of time (150-450 seconds mean) and changes abruptly at epoch boundaries.

23 pages

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