Computer Science Department
School of Computer Science, Carnegie Mellon University


The Statistical Properties of Host Load
(Extended Version)

Peter A. Dinda

March 1999

A version of this paper will appear in Scientific Programming in Fall 1999. An earlier description of this work appeared in the Proceedings of the Fourth Workshop on Languages, Compilers, and Run-time Systems for Scalable Computer (LCR98) and as Carnegie Mellon Computer Science Department Technical Report CMU-CS-98-143.

Keywords: Host load properties, host load prediction, self-similarity, long-range dependence, 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 traces of the Digital Unix 5 second exponential load average on over 35 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 0.73 to 0.99, 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. Despite these complex behaviors, we have found that relatively simple linear models are sufficient for short-range host load prediction.

28 pages

Return to: SCS Technical Report Collection
School of Computer Science homepage

This page maintained by