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On the Self-Similar Nature of Ethernet Traffic (1993)

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by Will E. Leland , Murad S. Taqqu , Walter Willinger , Daniel V. Wilson
Venue:IN PROC. OF THE ACM SIGCOMM’93
Citations:1578 - 43 self
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User correction supplied by mph

DatumValueSource
TITLE On the Self-Similar Nature of Ethernet Traffic user correction
AUTHOR NAME Will E. Leland user correction
AUTHOR NAME Murad S. Taqqu user correction
AUTHOR NAME Walter Willinger user correction
AUTHOR NAME Daniel V. Wilson user correction
AUTHOR AFFIL †Bellcore; §Department of Mathematics; 445 South Street user correction
AUTHOR ADDR Boston University; Morristown, NJ 07960-6438 Boston, MA 02215 user correction
ABSTRACT We demonstrate that Ethernet local area network (LAN) traffic is statistically self-similar, that none of the commonly used traffic models is able to capture this fractal behavior, and that such behavior has serious implications for the design, control, and analysis of high-speed, cell-based networks. Intuitively, the critical characteristic of this self-similar traffic is that there is no natural length of a "burst": at every time scale ranging from a few milliseconds to minutes and hours, similar-looking traffic bursts are evident; we find that aggregating streams of such traffic typically intensifies the self-similarity ("burstiness") instead of smoothing it. Our conclusions are supported by a rigorous statistical analysis of hundreds of millions of high quality Ethernet traffic measurements collected between 1989 and 1992, coupled with a discussion of the underlying mathematical and statistical properties of self-similarity and their relationship with actual network behavior. We also consider some implications for congestion control in high-bandwidth networks and present traffic models based on self-similar stochastic processes that are simple, accurate, and realistic for aggregate traffic. user correction
YEAR 1993 INFERENCE
VENUE IN PROC. OF THE ACM SIGCOMM’93 user correction
VENUE TYPE CONFERENCE INFERENCE
CITATIONS 24 found ParsCit 1.0
The National Science Foundation
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