Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
article
Free access

The changing nature of network traffic: scaling phenomena

Published: 01 April 1998 Publication History

Abstract

In this paper, we report on some preliminary results from an in-depth, wavelet-based analysis of a set of high-quality, packet-level traffic measurements, collected over the last 6-7 years from a number of different wide-area networks (WANs). We first validate and confirm an earlier finding, originally due to Paxson and Floyd [14], that actual WAN traffic is consistent with statistical self-similarity for sufficiently large time scales. We then relate this large-time scaling phenomenon to the empirically observed characteristics of WAN traffic at the level of individual connections or applications. In particular, we present here original results about a detailed statistical analysis of Web-session characteristics, and report on an intriguing scaling property of measured WAN traffic at the transport layer (i.e., number of TCP connection arrivals per time unit). This scaling property of WAN traffic at the TCP layer was absent in the pre-Web period but has become ubiquitous in today's WWW-dominated WANs and is a direct consequence of the ever-increasing popularity of the Web (WWW) and its emergence as the major contributor to WAN traffic. Moreover, we show that this changing nature of WAN traffic can be naturally accounted for by self-similar traffic models, primarily because of their ability to provide physical explanations for empirically observed traffic phenomena in a networking context. Finally, we provide empirical evidence that actual WAN traffic traces also exhibit scaling properties over small time scales, but that the small-time scaling phenomenon is distinctly different from the observed large-time scaling property. We relate this newly observed characteristic of WAN traffic to the effects that the dominant network protocols (e.g., TCP) and controls have on the flow of packets across the network and discuss the potential that multifractals have in this context for providing a structural modeling approach for WAN traffic and for capturing in a compact and parsimonious manner the observed scaling phenomena at large as well as small time scales.

References

[1]
{1} P. Abry and D. Veitch. Wavelet analysis of long-range dependent traffic. IEEE Transactions on Information Theory 44, pp. 2-15, 1998.
[2]
{2} M. E. Crovella and A. Bestavros. Self-similarity in world wide web traffic - evidence and possible causes. Proceedings of Sigmetrics'96, pp. 160-169, 1996.
[3]
{3} D. R. Cox. Long-range dependence: A review. In H. A. David and H. T. David, editors, Statistics: An Appraisal, pages 55-74. Iowa State University Press, Ames, Iowa, 1984.
[4]
{4} I. Daubechies. Ten lectures on wavelets., SIAM, Philadelphia, 1992.
[5]
{5} C. J. G. Evertsz and B. B. Mandelbrot. Multifractal measures. In H. -O. Peitgen, H. Jurgens and D. Saupe, editors, Chaos and Fractals: New Frontiers in Science, Springer-Verlag, New York, 1992.
[6]
{6} A. Feldmann. On-line call admission for high-speed networks. Ph.D. Thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 1995.
[7]
{7} A. Feldmann. Modelling characteristics of TCP connections. Preprint, 1996.
[8]
{8} V. K. Gupta and E. C. Waymire. A statistical analysis of mesoscale rainfall as a random cascade. Journal of Applied Meteorology 32, pp. 251-267, 1993.
[9]
{9} R. Holley and E. C. Waymire. Multifractal dimensions and scaling exponents for strongly bounded random cascades. Annals of Applied Probability 2, pp. 819-845, 1992.
[10]
{10} G. Kaiser. A friendly guide to wavelets, Birkhäuser, Boston, 1994.
[11]
{11} T. G. Kurtz. Limit theorems for workload input models. In F. P. Kelly, S. Zachary, and I. Ziedins, editors, Stochastic Networks: Theory and Applications. Clarendon Press, Oxford, 1996.
[12]
{12} W. E. Leland, M. S. Taqqu, W. Willinger and D. V. Wilson. On the self-similar nature of Ethernet traffic (Extended Version). IEEE/ACM Transactions on Networking 2, pp. 1-15, 1994.
[13]
{13} Y. Meyer. Wavelets and operators, Cambridge University Press, Cambridge, UK, 1993.
[14]
{14} V. Paxson and S. Floyd. Wide area traffic: The failure of poisson modeling. IEEE/ACM Transactions on Networking 3, pp. 226-244, 1995.
[15]
{15} R. H. Riedi and J. Levy Vehel. Multifractal properties of TCP traffic: A numerical study. Preprint, 1997.
[16]
{16} W. Stevens. TCP/IP illustrated, Volume 1: The Protocols, Addison-Wesley, Reading, MA, 1994.
[17]
{17} M. S. Taqqu, V. Teverovsky and W. Willinger. Is network traffic self-similar or multifractal? Fractals 5, pp. 63-73, 1997.
[18]
{18} W. Willinger, M. S. Taqqu, and A. Erramilli. A bibliographical guide to self-similar traffic and performance modeling for modern high-speed networks. In F. P. Kelly, S. Zachary, and I. Ziedins, editors, Stochastic Networks: Theory and Applications, pages 339-366. Clarendon Press, Oxford, 1996.
[19]
{19} W. Willinger, V. Paxson, and M. S. Taqqu. Self-similarity and heavy tails: Structural modeling of network Traffic. To appear in R. Adler, R. Feldman, and M. S. Taqqu, editors, A Practical Guide to Heavy Tails: Statistical Techniques for Analyzing Heavy Tailed Distributions, Birkhauser Verlag, Boston.
[20]
{20} W. Willinger, M. S. Taqqu, R. Sherman, and D. V. Wilson. Self-similarity through high-variability: Statistical analysis of Ethernet LAN traffic at the source level. IEEE/ACM Transactions on Networking 5, pp. 71-86, 1997.

Cited By

View all
  • (2024)Newton Sketches: Estimating Node Intimacy in Dynamic Graphs Using Newton's Law of Cooling2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00225(2904-2916)Online publication date: 13-May-2024
  • (2023)Joint DNN partitioning and task offloading in mobile edge computing via deep reinforcement learningJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00493-912:1Online publication date: 3-Aug-2023
  • (2023)Approximate waiting times for queuing systems with variable long-term correlated arrival ratesPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2023.128513614(128513)Online publication date: Mar-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 1998
Published in SIGCOMM-CCR Volume 28, Issue 2

Check for updates

Qualifiers

  • Article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)140
  • Downloads (Last 6 weeks)16
Reflects downloads up to 26 Sep 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Newton Sketches: Estimating Node Intimacy in Dynamic Graphs Using Newton's Law of Cooling2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00225(2904-2916)Online publication date: 13-May-2024
  • (2023)Joint DNN partitioning and task offloading in mobile edge computing via deep reinforcement learningJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00493-912:1Online publication date: 3-Aug-2023
  • (2023)Approximate waiting times for queuing systems with variable long-term correlated arrival ratesPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2023.128513614(128513)Online publication date: Mar-2023
  • (2022)Methods of Multidimensional Aggregation of Time Series of Streaming Data for Cyber-Physical System MonitoringAutomatic Control and Computer Sciences10.3103/S014641162208018156:8(829-837)Online publication date: 1-Dec-2022
  • (2022)On the Analysis and Predictability of Long-Term Correlated Traffic Patterns in a Wide Area Network2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)10.1109/SIBIRCON56155.2022.10016987(2000-2003)Online publication date: 11-Nov-2022
  • (2021)Supervised Learning of Neural Networks for Active Queue Management in the InternetSensors10.3390/s2115497921:15(4979)Online publication date: 22-Jul-2021
  • (2020)Long-range dependence and self-similarity of teletraffic with different protocols at the large time scale of day in the duration of 12 years: Autocorrelation modelingPhysica Scripta10.1088/1402-4896/ab82c495:6(065222)Online publication date: 15-Apr-2020
  • (2020)Fractal teletraffic delay bounds in computer networksPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2020.124903557(124903)Online publication date: Nov-2020
  • (2020)Multi-fractional generalized Cauchy process and its application to teletrafficPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2019.123982(123982)Online publication date: Jan-2020
  • (2019)Early Online Classification of Encrypted Traffic Streams using Multi-fractal FeaturesIEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)10.1109/INFCOMW.2019.8845127(84-89)Online publication date: Apr-2019
  • Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media