Abstract
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.
Effort sponsored in part by the Advanced Research Projects Agency and Rome Laboratory, Air Force Materiel Command, USAF, under agreement number F30602-96-1-0287, in part by the National Science Foundation under Grant CMS-9318163, and in part by a grant from the Intel Corporation. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Advanced Research Projects Agency, Rome Laboratory, or the U.S. Government.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arabe, J., Beguelin, A., Lowekamp, B., E. Seligman, M. S., and Stephan, P. Dome: Parallel programming in a heterogeneous multi-user environment. Tech. Rep. CMU-CS-95-137, Carnegie Mellon University, School of Computer Science, April 1995.
Bassingthwaighte, J. B., Beard, D. A., Percival, D. B., and Raymond, G. M. Fractal structures and processes. In Chaos and the Changing Nature of Science and Medicine: An Introduction (April 1995), D. E. Herbert, Ed., no. 376 in AIP Conference Proceedings, American Institute of Physics, pp. 54–79.
Beran, J. Statistical methods for data with long-range dependence. Statistical Science 7, 4 (1992), 404–427.
Box, G. E. P., Jenkins, G. M., and Reinsel, G. Time Series Analysis: Forecasting and Control, 3rd ed. Prentice Hall, 1994.
Brockwell, P. J., and Davis, R. A. Introduction to Time Series and Forecasting. Springer-Verlag, 1996.
Eager, D. L., Lazowska, E. D., and Zahorjan, J. The limited performance benefits of migrating active processes for load sharing. In SIGMETRICS’ 88 (May 1988), pp. 63–72.
Granger, C.W.J., and Joyeux, R. An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis 1, 1 (1980), 15–29.
Harchol-Balter, M., and Downey, A. B. Exploiting process lifetime distributions for dynamic load balancing. In Proceedings of ACM SIGMETRICS’ 96 (May 1996), pp. 13–24.
Hosking, J. R. M. Fractional differencing. Biometrika 68, 1 (1981), 165–76.
Hurst, H. E. Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers 116 (1951), 770–808.
Jain, R. The Art of Computer Systems Performance Analysis. John Wiley and Sons, Inc., 1991.
Jensen, E. D. A real-time manifesto. http://www.realtime-os.com, 1996.
Kaplan, J. A., and Nelson, M. L. A comparison of queueing, cluster, and distributed computing systems. Tech. Rep. NASA TM 109025 (Revision 1), NASA Langley Research Center, June 1994.
Leland, W. E., and Ott, T. J. Load-balancing heuristics and process behavior. In Proceedings of Performance and ACM SIGMETRICS (1986), vol. 14, pp. 54–69.
Leland, W. E., Taqqu, M. S., Willinger, W., and Wilson, D. V. On the self-similar nature of ethernet traffic. In Proceedings of ACM SIGCOMM’ 93 (September 1993).
Morin, P. R. The impact of self-similarity on network performance analysis. Tech. Rep. Computer Science 95.495, Carleton University, December 1995.
Mutka, M. W., and Livny, M. The available capacity of a privately owned workstation environment. Performance Evaluation 12, 4 (July 1991), 269–284.
Object Management Group. Realtime corba: A white paper. http://www.omg.org, December 1996. In Progess.
Polze, A., Fohler, G., and Werner, M. Predictable network computing. In Proceedings of the 17th International Conference on Distributed Computing Systems (ICDCS’ 97) (May 1997), pp. 423–431.
Rinard, M., Scales, D., and Lam, M. Jade: A high-level machine-independent language for parallel programming. IEEE Computer 26, 6 (June 1993), 28–38.
Shannon, C. E. A mathematical theory of communication. Bell System Tech. J. 27 (1948), 379–423, 623-656.
Siegell, B., and Steenkiste, P. Automatic generation of parallel programs with dynamic load balancing. In Proceedings of the Third International Symposium on High-Performance Distributed Computing (August 1994).
Taqqu, M. S., Teverovsky, V., and Willinger, W. Estimators for long-range dependence: An empirical study. Fractals 3, 4 (1995), 785–798.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dinda, P.A. (1998). The Statistical Properties of Host Load. In: O’Hallaron, D.R. (eds) Languages, Compilers, and Run-Time Systems for Scalable Computers. LCR 1998. Lecture Notes in Computer Science, vol 1511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49530-4_23
Download citation
DOI: https://doi.org/10.1007/3-540-49530-4_23
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65172-7
Online ISBN: 978-3-540-49530-7
eBook Packages: Springer Book Archive