Understanding the long-term self-similarity of internet traffic

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2156, Issue , 2001, Pages 286-298

  Uhlig, Steve ^a   Bonaventure, Olivier ^a  

^a UNIVERSITY OF NAMUR   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

INTERNET SERVICE PROVIDERS; WEB SERVICES;

EXTERNAL SOURCES; HEAVY-TAILED DISTRIBUTION; INTER-DOMAIN TRAFFIC; INTERNET TRAFFIC; SELF-SIMILAR; SELF-SIMILAR TRAFFIC; SELF-SIMILARITIES; TRAFFIC VOLUMES;

INTERNET;

EID: 84949971733     PISSN: 03029743     EISSN: 16113349     Source Type: Journal    
DOI: 10.1007/3-540-45412-8_20     Document Type: Article

References (17)

1. J. Beran. Statistics for Long-Memory Processes. Monographs on Statistics and Applied Probability, Chapman & Hall, 1994.
2. M. Crovella and A. Bestavros. Self-Similarit y in World Wide Web Traffic: Evidence and Possible Causes. In SIGMETRICS’96, pages 160-169, May 1996.
3. Cisco. Net Flow services and applications. White paper, available from http://www.cisco.com/warp/public/732/netflow, 1999.
4. B. Hill. A simple approach to inference about the tail of a distribution. Annals of Statistics, 3(1975), pages 1163-1174, 1975.
5. W. Leland, M. Taqqu, W. Willinger and D. Wilson. On the Self-Similar Nature of Ethernet Traffic (Extended Version). IEEE/ACM Transactions on Networking, February 1994.
6. V. Paxson and S. Floyd. Wide-Area Traffic: The Failure of Poisson Modeling. IEEE/ACM Transactions on Networking, 3(3):226-244, June 1995.
7. K. Park, G. Kim and M. Crovella. On the relationship between file sizes, transport protocols, and self-similar network traffic. In Proc. Fourth International Conference on Network Protocols, October 1996.
8. K. Park, G. Kim, and M. Crovella. On the effect of traffic self-similarity on network performance. In Proc.of SPIE International Conference on Performance and Control of Network Systems, November 1997.
9. S. Resnick. Heavy Tail Modeling and Teletraffic Data. Annals of Statistics, 25(1997), pages 1805-1869, 1997.
10. S. Resnick. Why Non-Linearities Can Ruin the Heavy-Tailed Modeler’s Day. In “A Practical Guide to Heavy Tails: Statistical Techniques and Applications”, Birkhauser, Boston, 1998.
11. S. Robert and J.-Y. Le Boudec. New models for self-similar traffic. Performance Evaluation 30(1-2), pages. 57-68, 1997.
12. S. McCreary and K. Claffy. Trends in wide area IP traffic patterns: a view from Ames Internet Exchange. Available from http://www.caida.org/outreach/papers/AIX0005/, 2000.
13. M. Taqqu, V. Teverovsky and W. Willinger. Estimators for long-range dependence: an empirical study. Fractals, (3):4:785-798, 1995.
14. M. Taqqu and G. Samorodnitsky. On Estimating the Intensity of Long-Range Dependence in Finite and Infinite Variance Time Series. In “A Practical Guide to Heavy Tails: Statistical Techniques and Applications”, Birkhauser, Boston, 1998.
15. M. Taqqu, W. Willinger, and R. Sherman. Proof of a fundamental result in self-similar traffic modeling. ACM/SIGCOMM Computer Communications Review, 27(1997), pages 5-23, 1997.
16. S. Uhlig and O. Bona venture. On the Cost of Using MPLS for Interdomain Traffic. In Proc.of QOFIS2000, Berlin, September 2000.
17. W. Willinger, V. Paxson, and M. Taqqu. Self-similarity and heavy tails: Structural modeling of network Traffic. In “A Practical Guide to Heavy Tails: Statistical Techniques and Applications”, Birkhauser Verlag, Boston, 1998.