Abstract
Simulations with web traffic usually generate input by sampling a heavy-tailed object size distribution. As a consequence these simulations remain in transient state over all periods of time, i.e. all statistics that depend on moments of this distribution, such as the average object size or the average user-perceived latency of downloads, do not converge within periods practically feasible for simulations. We therefore investigate whether the 95-th, 98-th, and 99-th latency percentiles, which do not depend on the extreme tail of the latency distribution, are more suitable statistics for the performance evaluation. We exploit that corresponding object size percentiles in samples from a heavy-tailed distribution converge to normal distributions during periods feasible for simulations. Conducting a simulation study with ns-2, we find a similar convergence for network latency percentiles. We explain this finding with probability theory and propose a method to reliably test for this convergence.
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
W.E. Leland, M.S. Taqqu, W. Wilinger, and D.V. Wilson, “On the Self-Similar Nature of Ethernet Traffic (Extended Version),” IEEE/ACM Transactions on Networking, vol. 2, no. 1, pp. 1–15, Dec. 1994.
M. Crovella and A. Bestavros, “Self-Similarity in World Wide Web Traffic: Evidence and Possible Causes,” IEEE/ACM Transactions on Networking, vol. 5, no. 6, pp. 835–846, Dec. 1997.
A. Erramilli, O. Narayan, and W. Willinger, “Experimental Queueing Analysis with Long-Range Dependent Packet Traffic,” IEEE/ACM Transactions on Networking, vol. 4, no. 2, pp. 209–223, Apr. 1996.
K. Park, G. T. Kim, and M. E. Crovella, “On the Relationship between File Sizes, Transport Protocols, and Self-Similar Network Traffic,” in Proceedings of the Fourth International Conference on Network Protocols (ICNP’96), Columbus, Ohio, USA, Oct. 1996, pp. 171–180.
M. Crovella and L. Lipsky, “Simulations with Heavy-Tailed Workloads,” in Self-Similar Network Traffic and Performance Evaluation, K. Park and W. Willinger, Eds., chapter 3, pp. 89–100. Wiley-Interscience, NY, 2000.
U. Fiedler, P. Huang, and B. Plattner, “Towards Provisioning Diffserv Intra-Nets,” in Proceedings of IWQoS’01, Karlsruhe, Germany, June 2001, pp. 27–43, Springer.
A. M. Odlyzko, “The Internet and other Networks: Utilization Rates and their Implications,” Information Economics and Policy, vol. 12, pp. 341–365, 2000.
S. Ben Fredj et. al., “Statistical Bandwidth Sharing: A Study of Congestion at Flow Level,” in Proceedings of SIGCOMM’01, San Diego, California, USA, Aug. 2001, pp. 111–122, ACM.
P. Barford and M. Crovella, “Generating Representative Web Workloads for Network and Server Performance Evaluation,” in Proceedings of Performance’ 98/ACM SIGMETRICS’ 98, Madison, Wisconsin, USA, June 1998, pp. 151–160.
A. Feldmann et. al., “Dynamics of IP traffic: A Study of the Role of Variability and the Impact of Control,” in Proceedings of SIGCOMM’99, Cambridge, Massachusetts, USA, Sept. 1999, ACM.
Walter Willinger, Murad S. Taqqu, Robert Sherman, and Daniel V. Wilson, “Self-Similarity through High-Variability: Statistical Analysis of Ethernet LAN Traffic at the Source Level,” IEEE/ACM Transactions on Networking, vol. 5, no. 1, pp. 71–86, 1997.
C. Goldie and C. Kluppelberg, “Subexponential Distributions,” in A Practical Guide to Heavy Tails: Statistical Techniques for Analysing Heavy Tails, R. Feldman R. Adler and M.S. Taqqu, Eds., pp. 435–460. Birkhauser, Basel (CH), 1997.
Norman L. Johnson, Samuel Kotz, and N. Balakrishnan, Continuous Univariate Distributions, vol. 1 of Wiley Series in Probability and Mathematical Statistics, Wiley, NY, 2 edition, 1994.
J. Rice, Mathematical Statistics and Data Analysis, 2nd edition, Duxbury Press, 1995.
C. Radhakrishna Rao, Linear Statistical Inference and Its Applications, Wiley, New York, 2 edition, 1973.
Frank R. Hampel, Elvezio M. Ronchetti, Peter J. Rousseeuw, and Werner A. Stahel, Robust Statistics: The Approach Based on Influence Functions, Wiley, NY, 1986.
Jan Beran, Statistics for Long-Memory Processes, Chapman & Hall, NY, 1994.
L. Breslau et. al., “Advances in Network Simulations,” IEEE Computer, May 2000.
R. Fielding et. al., “Hypertext Transfer Protocol — HTTP/1.1,” RFC 2616, Internet Request For Comments, June 1999.
S. Bajaj et. al., “Is Service Priority Useful in Networks?,” in Proceedings of the ACM Sigmetrics’ 98, Madison, Wisconsin USA, June 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fiedler, U., Plattner, B. (2003). Using Latency Quantiles to Engineer QoS Guarantees for Web Services. In: Jeffay, K., Stoica, I., Wehrle, K. (eds) Quality of Service — IWQoS 2003. IWQoS 2003. Lecture Notes in Computer Science, vol 2707. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44884-5_19
Download citation
DOI: https://doi.org/10.1007/3-540-44884-5_19
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40281-7
Online ISBN: 978-3-540-44884-6
eBook Packages: Springer Book Archive