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Self-similarity through high-variability: statistical analysis of ethernet LAN traffic at the source level
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Source Applications, Technologies, Architectures, and Protocols for Computer Communication archive
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication table of contents
Cambridge, Massachusetts, United States
Pages: 100 - 113  
Year of Publication: 1995
ISBN:0-89791-711-1
Also published in ...
Authors
Walter Willinger  Bellcore
Murad S. Taqqu  Boston University
Robert Sherman  Bellcore
Daniel V. Wilson  Bellcore
Sponsor
SIGCOMM: ACM Special Interest Group on Data Communication
Publisher
ACM  New York, NY, USA
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ABSTRACT

A number of recent empirical studies of traffic measurements from a variety of working packet networks have convincingly demonstrated that actual network traffic is self-similar or long-range dependent in nature (i.e., bursty over a wide range of time scales) - in sharp contrast to commonly made traffic modeling assumptions. In this paper, we provide a plausible physical explanation for the occurrence of self-similarity in high-speed network traffic. Our explanation is based on convergence results for processes that exhibit high variability (i.e., infinite variance) and is supported by detailed statistical analyses of real-time traffic measurements from Ethernet LAN's at the level of individual sources.Our key mathematical result states that the superposition of many ON/OFF sources (also known as packet trains) whose ON-periods and OFF-periods exhibit the Noah Effect (i.e., have high variability or infinite variance) produces aggregate network traffic that features the Joseph Effect (i.e., is self-similar or long-range dependent). There is, moreover, a simple relation between the parameters describing the intensities of the Noah Effect (high variability) and the Joseph Effect (self-similarity). An extensive statistical analysis of two sets of high time-resolution traffic measurements from two Ethernet LAN's (involving a few hundred active source-destination pairs) confirms that the data at the level of individual sources or source-destination pairs are consistent with the Noah Effect. We also discuss implications of this simple physical explanation for the presence of self-similar traffic patterns in modern high-speed network traffic for (i) parsimonious traffic modeling (ii) efficient synthetic generation of realistic traffic patterns, and (iii) relevant network performance and protocol analysis.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
 
2
F. Brichet, J Roberts, A. Simonian, and D. Veitch. Heavy traffic analysis of a fluid queue fed by on/off sources with long-range dependence. Preprint, 1995.
 
3
F. Danzig, S Jam,n, R. C~ceres, D. Mitzel and D. Estrin. An Empirical Workload Model for Driving Wide-Area TCP/IP Network Simulations. Internetwork~ng: Research and Experience, Vol. 3, pp. 1-26, 1992.
 
4
N.G. Duffield. Economies of scale in queues with sources having power-law large deviation scalings. Preprint, 1994.
 
5
N G. Duffield and N O'Connell. Large deviations and overflow probabilities for the general single-server queue, with applications. Prec. Cambridge Phil. Soc,~ 1995 (to appear).
 
6
 
7
A. Erramilli, O. Narayan, and W. Willinger. Experimental queueing analysis with long-range dependent traffic. Preprint, 1994.
 
8
 
9
R. Gusella. A measurement study of diskless workstation traffic on an Ethernet. IEEE Transactions on Comraun~cations, Vol. 38, pp. 1557-1568, 1990.
 
10
R. Gusella. Characterizing the variability of arrival processes with indexes of dispersion. IEEE Journal on Selected Areas tn Cornmunzcat, ons, Vol. 9, pp. 203-211, 1991.
 
11
B. M. Hill. A simple general approach to reference about the tail of a distribution The Annals of Stat,stzcs, Vol. 3, pp 1163- 1174, 1975.
 
12
R. Jain and S. A. Routhier. Packet trains: Measurements and a new model for Computer network traffic. IEEE Journal on Selected Areas in Coraraunicattons, Vol. 4, pp 986-995, 1986.
 
13
S. M. Klivansky, A. Mukherjee, and C. Song. On Long-Range Dependence in NSFNET Traffic. Preprint, 1994.
 
14
M. F. Kratz and S. I. Resnick. The QQ-Estimator and Heavy Tails. Preprint, 1995.
 
15
16
 
17
18
 
19
B.B. Mandelbrot. Long-run linearity, locally Gaussian processes, H-spectra and infinite variances. Internatzonal Economic Remew, Vol. 10, pp. 82-113, 1969.
 
20
B. B. Mandelbrot. The Fractat Geometry of Nature. Freeman, New York, 1983.
 
21
K. Meier-Hellstern, P. E. Wirth, Y.-L. Yah, and D. A. Hoefiin. Traffic models for ISDN data users: Office Automation application. In A. Jensen and V. B. Iversen, editors, Teletraffic and Datatraj~c m a Pemod of Change, Proc of ITC13, Copenhagen, pp. 167-172, Amsterdam, 1991, Elsevier Science Publishers B. V.
 
22
I. Norros. A storage model with self-similar input. Queueing Systems, Vol. 16, pp. 387-396, 1994.
 
23
V. Paxson. Growth Trends in Wide-Area TCP Connections IEEE Network, Vol. 8, pp. 8-17, 1994.
24
 
25
P. Pruthi and A. Erramilli. Heavy-tailed on/off source behavior and self-similar traffic. Prec. IEEE ICC'95, Seattle, June 1995.
 
26
S. I. Resnick and C. Starcia. Smoothing the Hill Estimator Preprint, 1995.
 
27
G. Samorodnitsky and M. S. Taqqu. Stable Non-Gausszan Processes: Stochasttc Models with Infinite Variance. Chapman and Hall, New York, London, 1994.
 
28
D. F. Swayne, D. Cook, and A. Buja. XGobi: Interactive Dynamic Graphics in the X Window System with a Link to S. 1991 Proceedings of the Section on Stat~sttcal Graphzcs, pp. 1-8, 1991.
 
29
M. S. Taqqu and j. Levy. Using renewal processes to generate long-range depm~dence and high variability. In E. Eberlem ~nd M. S. Taqqu, editors, Dependence in Probab~hty and Stat,stzcs, pp. 73-89, Boston, 1986. Birkhiiuser.
 
30
J W. Tukey and P. A Tukey Strips displaying empirical distributions: I. Textured Dot Strips. Bellcore Technzcal Memorandum, 1990.

CITED BY  65

Collaborative Colleagues:
Walter Willinger: colleagues
Murad S. Taqqu: colleagues
Robert Sherman: colleagues
Daniel V. Wilson: colleagues