Traffic Modeling in PLC Networks using a Markov Fluid Model with Autocorrelation Function Fitting

Abstract

Abstract. In this paper, we present an analysis of VoIP (Voice over IP) traffic and data transfer using PLC (PowerLine Communications) network. We propose a model based on MMFM (Markov Modulated Fluid Models) for data and VoIP traffic in PLC networks. Simulations and comparisons were carried out to verify the efficiency of the proposed traffic model over the Poisson and MMPP (Markov Modulated Poisson Process) models.

Estimation of quality of service parameters for OFDM–TDMA networks using Markov chains and service curve

“…The next state depends on the current state. The number of states and the transition rates are determined from the mean, variance and the autocovariance function of the network traffic trace [11]. In the MMFM, the exponential rate ρ i,j of transition from state i to state j is given by:…”

“…That is, the autocovariance function of the traffic trace is given by (12) which is adjusted according to (11) where a = α + β, that is:…”

“…A Queueing based model for an OFDM-TDMA system can be established, as in [10]. However, to guarantee reliability of the network traffic representation, in this work we propose the use of a model based on Queueing Theory where the user packet arrival process is modeled by a MMFM with autocorrelation function fitted according to the considered real traffic traces [11]. We propose a Markovian Model to network traffic traces instead of, for example a multifractal model, in order to attain some requirements to estimate the parameters of the Queuing Model such as input process stationarity and a regular Markov chain.…”