Segment-based adaptive hyper-Erlang model for long-tailed network traffic approximation

Journal of Supercomputing

Volumn 45, Issue 3, 2008, Pages 296-312

  Wang, Junfeng ^a   Liu, Jin ^b   She, Chundong ^c  

^a SICHUAN UNIVERSITY   (China)

^b WUHAN UNIVERSITY   (China)

^c CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Adaptive approximation; Expectation maximization (EM) algorithm; Hyper Erlang distribution; Network traffic

Indexed keywords

ADAPTIVE APPROXIMATION; ADAPTIVE FRAMEWORK; ADAPTIVE MODELING; DIVIDE-AND-CONQUER; ERLANG DISTRIBUTIONS; ERLANG-MODEL; EXPECTATION MAXIMIZATION (EM) ALGORITHM; HYPER-ERLANG DISTRIBUTION; HYPEREXPONENTIAL; MODEL PARAMETERS; NETWORK TRAFFIC; NETWORK TRAFFICS; PERFORMANCE EVALUATION; PHASE-TYPE DISTRIBUTIONS; SEGMENT-BASED; TRAFFIC DATA;

INTERNET;

EID: 49149112026     PISSN: 09208542     EISSN: 15730484     Source Type: Journal    
DOI: 10.1007/s11227-008-0173-5     Document Type: Article

References (26)

1. Gomes CP, Selman B, Kautz H (2000) Heavy-tailed phenomena in satisfiability and constraint satisfaction problems. J Autom Reas 24(1-2):67-100
2. Hernández-Campos F, Marron JS, Samorodnitsky G et al. (2004) Variable heavy tails in Internet traffic. Perform Eval 58:261-284
3. Casanova H (2005) Network modeling issues for grid application scheduling. Int J Found Comput Sci 16(2):145-162
4. Feitelson DG (2000) Sensitivity of parallel job scheduling to fat-tailed distributions. Technical Report 2000-44, The Hebrew University of Jerusalem, October 2000
5. Kleban SD, Clearwater SH (2003) Hierarchical dynamics, interarrival times, and performance. In: Proceedings of the 15th ACM/IEEE conference on high performance networking and computing (SC 2003), Phoenix, AZ, USA, November 2003
6. Ata S, Murata M, Miyahara H (2000) Analysis of network traffic and its application to design of high-speed routers. IEICE Trans Inf Syst E83-D(5):988-995
7. Starobinski D, Sidi M (2000) Modeling and analysis of power-tail distributions via classical teletraffic methods. Queuing Syst 36(1-3):243-267
8. Asaka T, Ori K, Yamamoto H (2003) Method of estimating flow duration distribution using active measurements. IEICE Trans Commun E86-B(10):3030-3037
9. Downey AB (2001) Evidence for long-tailed distributions in the Internet. In: Proceedings of ACM SIGCOMM Internet measurement workshop (IMW 2001), San Diego, CA, USA, November 2001
10. Bobbio A, Cumani A (1992) ML estimation of the parameters of a PH distribution in triangular canonical form. Perform Eval, 33-46
11. Feldmann A, Whitt W (1998) Fitting mixtures of exponentials to long-tailed distributions to analyze network performance models. Perform Eval 31(3-4):245-279
12. Wang J, Zhou H, Li L et al. (2006) A general model for long-tailed network traffic approximation. Springer J Supercomput 38(2):155-172
13. El Abdouni Khayari REA, Sadre R, Haverkort BR (2003) Fitting world-wide web request traces with the EM-algorithm. Perform Eval 52(2-3):175-191
14. Bilmes JA (1998) A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Technical Report, TR-97-021, International Computer Science Institute, Berkeley, CA
15. Horvath A, Telek M (2000) Approximating heavy tailed behavior with phase type distribution. Adv Algorithmic Methods Stoch Models, 191-214
16. Riska A, Diev V, Smirni E (2004) An EM-based technique for approximating long-tailed data sets with PH distributions. Perform Eval 55(1):147-164
17. Fang Y, Chlamtac I (1999) Teletraffic analysis and mobility modeling of PCS networks. IEEE Trans Commun 47(7):1062-1073
18. Kelly F (2006) Reversibility and stochastic networks. New York, Wiley. http://www.statslab.cam.ac.uk/frank/rsn.html
19. Figueiredo MAT, Leitão JMN, Jain AK (1999) On fitting mixture models. In: Proceedings of the second international workshop on energy minimization methods in computer vision and pattern recognition (EMMCVPR 1999). Lecture notes in computer science. Springer, Berlin, pp 54-69
20. Fraley C, Raftery A (1998) How many clusters? Which clustering method? Answers via model-based cluster analysis. Technical Report 329, Department of Statistics, University of Washington, Seattle, WA
21. Whindham M, Cutler A (1992) Information ratios for validating mixture analysis. J Am Stat Assoc 87:1188-1192
22. Rissanen J (1989) Stochastic complexity in statistical inquiry. World Scientific, Singapore
23. Jain A, Moreau J (1988) Algorithms for clustering data. Prentice Hall, Englewood Cliffs
24. Wang J, Zhou M, Zhou H (2004) Clock synchronization for Internet measurements: a clustering algorithm. Comput Netw 45:731-741
25. NASA HTTP traces (2007) http://ita.ee.lbl.gov/html/contrib/NASA-HTTP.html
26. Wireless traces (2007) http://crawdad.cs.dartmouth.edu/data/tang/final. anon.tar.gz