Modeling network traffic data by doubly stochastic point processes with self-similar intensity process and fractal renewal point process

S. Barbarossa, Anna Scaglione, A. Baiocchi, G. Colletti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper we propose a doubly stochastic point process for modeling traffic data. The traffic intensity is modeled as a self-similar process and is generated applying an inverse orthogonal wavelet transform to a sequence of independent random sequences, having different variances at different scales. The underlying point process is characterized by a fractal renewal point process of dimension less than one. The proposed model is intrinsically able to synthesize a point process characterized by arrivals packed into sparsely located clusters separated by occasionally very long interarrival times. This behavior is often encountered on real traffic data and it deserves a particular attention because is often the main responsible for packet losses and thus directly affects the network performance. The model is validated comparing the packet loss rate of a queueing buffer element driven by real and simulated traffic.

Original languageEnglish (US)
Title of host publicationConference Record of the Asilomar Conference on Signals, Systems and Computers
EditorsM.P. Farques, R.D. Hippenstiel
PublisherIEEE Comp Soc
Pages1112-1116
Number of pages5
Volume2
StatePublished - 1998
Externally publishedYes
EventProceedings of the 1997 31st Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) - Pacific Grove, CA, USA
Duration: Nov 2 1997Nov 5 1997

Other

OtherProceedings of the 1997 31st Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2)
CityPacific Grove, CA, USA
Period11/2/9711/5/97

Fingerprint

Packet loss
Fractals
Network performance
Wavelet transforms

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Barbarossa, S., Scaglione, A., Baiocchi, A., & Colletti, G. (1998). Modeling network traffic data by doubly stochastic point processes with self-similar intensity process and fractal renewal point process. In M. P. Farques, & R. D. Hippenstiel (Eds.), Conference Record of the Asilomar Conference on Signals, Systems and Computers (Vol. 2, pp. 1112-1116). IEEE Comp Soc.

Modeling network traffic data by doubly stochastic point processes with self-similar intensity process and fractal renewal point process. / Barbarossa, S.; Scaglione, Anna; Baiocchi, A.; Colletti, G.

Conference Record of the Asilomar Conference on Signals, Systems and Computers. ed. / M.P. Farques; R.D. Hippenstiel. Vol. 2 IEEE Comp Soc, 1998. p. 1112-1116.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Barbarossa, S, Scaglione, A, Baiocchi, A & Colletti, G 1998, Modeling network traffic data by doubly stochastic point processes with self-similar intensity process and fractal renewal point process. in MP Farques & RD Hippenstiel (eds), Conference Record of the Asilomar Conference on Signals, Systems and Computers. vol. 2, IEEE Comp Soc, pp. 1112-1116, Proceedings of the 1997 31st Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2), Pacific Grove, CA, USA, 11/2/97.
Barbarossa S, Scaglione A, Baiocchi A, Colletti G. Modeling network traffic data by doubly stochastic point processes with self-similar intensity process and fractal renewal point process. In Farques MP, Hippenstiel RD, editors, Conference Record of the Asilomar Conference on Signals, Systems and Computers. Vol. 2. IEEE Comp Soc. 1998. p. 1112-1116
Barbarossa, S. ; Scaglione, Anna ; Baiocchi, A. ; Colletti, G. / Modeling network traffic data by doubly stochastic point processes with self-similar intensity process and fractal renewal point process. Conference Record of the Asilomar Conference on Signals, Systems and Computers. editor / M.P. Farques ; R.D. Hippenstiel. Vol. 2 IEEE Comp Soc, 1998. pp. 1112-1116
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