Statistical Model of Network Traffic I. Antoniou1,2, V.V. Ivanov1,3, Valery V. Ivanov3,4 , P.V. Zrelov3 1Solvay Institutes for Physics and Chemistry, Brussels, Belgium A nonlinear analysis has been applied to traffic measurements obtained at the input of a medium size LAN [1]. Reliable values of the time lag and the embedding dimension provided a possibility to reconstruct the underlying dynamical system applying an artificial neural network (ANN). The ANN reproduced the statistical distribution of real data, which fits well the log-normal form. The detailed analysis of traffic measurements has shown that the log-normal distribution appears due to aggregation of real data [2]. The Principal Component Analysis of traffic series demonstrated that the first few components already form the main contribution to network traffic, while the residual components play a role of small irregular variations that can be interpreted as stochastic noise [3]. This result has also been confirmed by the application of wavelet filtering and Fourier analysis both to original traffic measurements and to individual principal components of original and filtered data [4]. The applicability of the A.Kolmogorov scheme [5] for homogeneous fragmentation of grains to network traffic is discussed. [1] P.Akritas, P.G.Akishin, I.Antoniou, A.Yu.Bonushkina,
I.Drossinos, V.V.Ivanov, Yu.L.Kalinovsky and P.V.Zrelov: Nonlinear
Analysis of Network Traffic, "Chaos, Solitons and Fractals",
Vol.14(4)(2002)595-606. |