Their Share: Diversity and Disparity in IP Traffic

The need to service populations of high diversity in the face of high disparity affects all aspects of network operation: planning, routing, engineering, security, and accounting. We analyze diversity/disparity from the perspective of selecting a boundary between mice and elephants in IP traffic aggregated by route, e.g., destination AS. Our goal is to find a concise quantifier of size disparity for IP addresses, prefixes, policy atoms and ASes, similar to the oft-quoted 80/20 split (e.g., 80% of volume in 20% of sources). We define crossover as the fraction c of total volume contributed by a complementary fraction 1-c of large objects. Studying sources and sinks at two Tier 1 backbones and one university, we find that splits of 90/10 and 95/5 are common for IP traffic. We compare the crossover diversity to common analytic models for size distributions such as Pareto/Zipf. We find that AS traffic volumes (by byte) are top-heavy and can only be approximated by Pareto with α=0.5, and that empirical distributions are often close to Weibull with shape parameter 0.2–0.3. We also find that less than 20 ASes send or receive 50% of all traffic in both backbones’ samples, a disparity that can simplify traffic engineering. Our results are useful for developers of traffic models, generators and simulators, for router testers and operators of high-speed networks.

[1]  Michael Mitzenmacher,et al.  A Brief History of Generative Models for Power Law and Lognormal Distributions , 2004, Internet Math..

[2]  Andrew M. Odlyzko,et al.  Privacy, economics, and price discrimination on the Internet , 2003, ICEC '03.

[3]  Evi Nemeth,et al.  Internet expansion, refinement and churn , 2002, Eur. Trans. Telecommun..

[4]  George C. Polyzos,et al.  A Parameterizable Methodology for Internet Traffic Flow Profiling , 1995, IEEE J. Sel. Areas Commun..

[5]  David Moore,et al.  The CoralReef Software Suite as a Tool for System and Network Administrators , 2001, LISA.

[6]  kc claffy,et al.  Understanding Internet traffic streams: dragonflies and tortoises , 2002, IEEE Commun. Mag..

[7]  Evi Nemeth,et al.  Spectroscopy of private DNS update sources , 2003, Proceedings the Third IEEE Workshop on Internet Applications. WIAPP 2003.

[8]  Ratul Mahajan,et al.  Controlling high bandwidth aggregates in the network , 2002, CCRV.

[9]  G. Yule,et al.  A Mathematical Theory of Evolution, Based on the Conclusions of Dr. J. C. Willis, F.R.S. , 1925 .

[10]  kc claffy,et al.  Analysis of RouteViews BGP data: policy atoms , 2001 .

[11]  Kimberly Claffy,et al.  Internet traffic characterization , 1994 .

[12]  George Varghese,et al.  Automatically inferring patterns of resource consumption in network traffic , 2003, SIGCOMM '03.

[13]  Anees Shaikh,et al.  Load-sensitive routing of long-lived IP flows , 1999, SIGCOMM '99.

[14]  Stefan Savage,et al.  Inferring Internet denial-of-service activity , 2001, TOCS.

[15]  Konstantina Papagiannaki,et al.  Impact of flow dynamics on traffic engineering design principles , 2004, IEEE INFOCOM 2004.

[16]  William Allen Simpson,et al.  PPP in HDLC-like Framing , 1994, RFC.

[17]  Nick Feamster,et al.  Guidelines for interdomain traffic engineering , 2003, CCRV.

[18]  Azer Bestavros,et al.  Self-similarity in World Wide Web traffic: evidence and possible causes , 1996, SIGMETRICS '96.

[19]  Mark Crovella,et al.  Self-Similarity in World Wide Web Traffic: Evidence and Causes , 1996, SIGMETRICS.

[20]  John Heidemann,et al.  On the correlation of Internet flow characteristics , 2003 .

[21]  Ratul Mahajan,et al.  Controlling High Bandwidth Aggregates in the Network (Extended Version) , 2001 .