Small and large scale behavior of moments of Poisson cluster processes
ESAIM: Probability and Statistics, Tome 21 (2017), pp. 369-393.

Poisson cluster processes are special point processes that find use in modeling Internet traffic, neural spike trains, computer failure times and other real-life phenomena. The focus of this work is on the various moments and cumulants of Poisson cluster processes, and specifically on their behavior at small and large scales. Under suitable assumptions motivated by the multiscale behavior of Internet traffic, it is shown that all these various quantities satisfy scale free (scaling) relations at both small and large scales. Only some of these relations turn out to carry information about salient model parameters of interest, and consequently can be used in the inference of the scaling behavior of Poisson cluster processes. At large scales, the derived results complement those available in the literature on the distributional convergence of normalized Poisson cluster processes, and also bring forward a more practical interpretation of the so-called slow and fast growth regimes. Finally, the results are applied to a real data trace from Internet traffic.

Reçu le :
Accepté le :
DOI : 10.1051/ps/2017018
Classification : 60G55, 60G18, 60K30, 60G22
Mots clés : Poisson cluster process, scaling, moments, cumulants, heavy tails, slow growth regime, fast growth regime, Internet traffic modeling
Antunes, Nelson 1 ; Pipiras, Vladas 2 ; Abry, Patrice 3 ; Veitch, Darryl 4

1 Center for Computational and Stochastic Mathematics, University of Lisbon, and University of Algarve, Campus de Gambelas, 8005–139 Faro, Portugal.
2 Department of Statistics and Operations Research, University of North Carolina, CB 3260, Chapel Hill, NC 27599, USA.
3 Univ Lyon, Ens de Lyon, Univ Claude Bernard, CNRS, Laboratoire de Physique, 69342 Lyon, France.
4 School of Computing and Communications, University of Technology Sydney, P.O. Box 123, Broadway, NSW 2007, Australia.
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Antunes, Nelson; Pipiras, Vladas; Abry, Patrice; Veitch, Darryl. Small and large scale behavior of moments of Poisson cluster processes. ESAIM: Probability and Statistics, Tome 21 (2017), pp. 369-393. doi : 10.1051/ps/2017018. http://www.numdam.org/articles/10.1051/ps/2017018/

P. Abry, R. Baraniuk, P. Flandrin, R. Riedi and D. Veitch, Multiscale nature of network traffic. IEEE Signal Proc. Magazine 3 (2002) 28–46. | DOI

P. Abry, P. Borgnat, F. Ricciato, A. Scherrer and D. Veitch, Revisiting an old friend: on the observability of the relation between long range dependence and heavy tail. Telecommun. Syst. 43 (2010) 147–165. | DOI

N. Antunes and V. Pipiras, Estimation of Flow Distributions from Sampled Traffic. ACM Trans. Model. Perform. Eval. Comput. Syst. 1 (2016) 11:1–11:28. | DOI

M.S. Bartlett, The spectral analysis of point processes. J. Roy. Stat. Soc. Series B. Methodological 25 (1963) 264–296. | MR | Zbl

P. Carruthers, E.M. Friedlander, C.C. Shih and R.M. Weiner, Multiplicity fluctuations in finite rapidity windows. Intermittency or quantum statistical correlation? Phys. Lett. B 222 (1989) 487–492. | DOI

D.R. Cox and V. Isham, Point Processes. Monographs on Applied Probability and Statistics. Chapman and Hall, London-New York (1980) | MR | Zbl

D.J. Daley and D. Vere–Jones, An Introduction to the Theory of Point Processes. Elementary Theory and Methods. Vol. I, Probability and its Applications (New York), 2nd edn. Springer-Verlag, New York (2003). | MR | Zbl

E.A. De Wolf, I.M. Dremin and W. Kittel, Scaling laws for density correlations and fluctuations in multiparticle dynamics. Phys. Rep. 270 (1996) 1–141. | DOI

C. Dombry and I. Kaj, Moment measures of heavy-tailed renewal point processes: asymptotics and applications. ESAIM. PS 17 (2013) 567–591. | DOI | Numdam | MR | Zbl

V. Fasen and G. Samorodnitsky, A fluid cluster Poisson input process can look like a fractional Brownian motion even in the slow growth aggregation regime. Adv. Appl. Prob. 41 (2009) 393–427. | DOI | MR | Zbl

G. Faÿ, B. González–Arévalo, T. Mikosch and G. Samorodnitsky, Modeling teletraffic arrivals by a Poisson cluster process. Queueing Syst. 54 (2006) 121–140. | DOI | MR | Zbl

R. Gaigalas and I. Kaj, Convergence of scaled renewal processes and a packet arrival model. Bernoulli 9 (2003) 671–703. | DOI | MR | Zbl

B. González-Arévalo and J. Roy, Simulating a Poisson cluster process for Internet traffic packet arrivals. Comput. Commun. 33 (2010) 612–618. | DOI

F. Grüneis, M. Nakao and M. Yamamoto, Counting statistics of 1/f fluctuations in neuronal spike trains. Biol. Cybernet. 62 (1990) 407–413. | DOI

F. Grüneis, M. Nakao, M. Yamamoto, T. Musha and H. Nakahama, An interpretation of 1/f fluctuations in neuronal spike trains during dream sleep. Biol. Cybernet. 60 (1989) 161–169. | DOI

C.A. Guerin, H. Nyberg, O. Perrin, S. Resnick, H. Rootzén and C. Stărică, Empirical testing of the infinite source Poisson data traffic model. Stoch. Models 19 (2003) 56–199. | DOI | MR | Zbl

A. Gut, Stopped Random Walks, Springer Series in Operations Research and Financial Engineering, 2nd edn. Springer, New York. Limit theorems and applications (2009) | MR | Zbl

N. Hohn, D. Veitch and P. Abry, Cluster processes: a natural language for network traffic. IEEE Trans. Signal Process. 51 (2003) 2229–2244. | DOI | MR

I. Kaj, Stochastic Modeling in Broadband Communications Systems, SIAM Monographs on Mathematical Modeling and Comput.. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA. (2002) | MR | Zbl

A.F. Karr, Point Processes and their Statistical Inference. Vol. 7 of Probability: Pure and Applied, 2nd edn. Marcel Dekker, Inc., New York (1991). | MR | Zbl

P.M. Krishna, V. Gadre and U.B. Desai, Multifractal Based Network Traffic Modeling. Springer Science and Business Media (2012).

W.E. Leland, M.S. Taqqu, W. Willinger and D.V. Wilson, On the self-similar nature of Ethernet traffic (Extended version). IEEE/ACM Trans. Netw. 2 (1994) 1–15. | DOI

A.W.P. Lewis, A branching Poisson process model for the analysis of computer failure patterns (with discussion). J. Roy. Stat. Soc. Series B. Methodological 26 (1964) 398–456. | MR | Zbl

S.B. Lowen and M.C. Teich, Fractal-Based Point Processes. Wiley Series in Probability and Statistics. Wiley-Interscience [John Wiley and Sons], Hoboken, NJ (2005) | MR | Zbl

B. Mandelbrot, A case against the lognormal distribution. Fractals and Scaling in Finance’, Springer New York (1997) 252–269. | MR

T. Mikosch, S. Resnick, H. Rootzén and A. Stegeman, Is network traffic approximated by stable Lévy motion or fractional Brownian motion?. Ann. Appl. Prob. 12 (2002) 23–68. | DOI | MR | Zbl

T. Mikosch and G. Samorodnitsky, Scaling limits for cumulative input processes. Math. Oper. Res. 32 (2007) 890–918. | DOI | MR | Zbl

C. Onof, R.E. Chandler, A. Kakou, P. Northrop, H.S. Wheater and V. Isham, Rainfall modelling using Poisson-cluster processes: a review of developments. Stoch. Environ. Res. Risk Assess. 14 (2000) 384–411. | DOI | Zbl

G. Peccati and M.S. Taqqu, Wiener Chaos: Moments, Cumulants and Diagrams of Bocconi. Vol. 1 Springer Series, Springer (2011) | MR | Zbl

V.J. Ribeiro, Z.-L. Zhang, S. Moon and C. Diot, Small-time scaling behavior of Internet backbone traffic. Comput. Netw. 48 (2005) 315–334. | DOI

E.A.B. Saleh, and M.C. Teich, Multiplied-Poisson noise in pulse, particle, and photon detection. Proc. IEEE 70 (1982) 229–245. | DOI

D. Veitch, N. Hohn and P. Abry, Multifractality in TCP/IP traffic: the case against. Comput. Netw. 48 (2005) 293–313. | DOI

H. Wendt, P. Abry and S. Jaffard, Bootstrap for empirical multifractal analysis. IEEE Signal Process. Magazine 24 (2007) 38–48. | DOI

M. Westcott, Results in the asymptotic and equilibrium theory of Poisson cluster processes. J. Appl. Probl. 10 (1973) 807–823. | DOI | MR | Zbl

R. Willink, Relationships between central moments and cumulants, with formulae for the central moments of gamma distributions. Commun. Statist. Theory Methods 32 (2003) 701–704. | DOI | MR | Zbl

P. Zeephongsekul, G. Xia and S. Kumar, Software reliability growth models based on cluster point processes. Int. J. Sys. Sci. 25 (1994) 737–751. | DOI | MR | Zbl

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