Multifractal Traffic Model Research Articles

Delay bound estimation for multicarrier 5G systems considering lognormal beta traffic envelope and stochastic service curve

AbstractIn this article, we propose an approach for estimating delay bound on user data in the downlink of wireless multicarrier 5G networks. To this end, we propose a stochastic arrival curve based on the parameters of a lognormal beta multifractal traffic model. More specifically, we present a delay bound equation that takes into account this novel stochastic arrival curve and a stochastic service curve for multicarrier systems. Simulations are carried out to confirm that the precision of the delay bound estimation using network calculus is enhanced by considering the proposed arrival envelope and service curve.

Estimation of Backlog and Delay in OFDM/ TDMA Systems with Traffic Policing Through Network Calculus

In this paper, we firstly propose a policing algorithm based on multifractal traffic modeling, evaluating its performance in comparison to other algorithms. Further, we present an approach based on Network Calculus for estimating quality of service parameters, such as mean queue length (backlog) and delay. We analyze these two parameters for an OFDM/TDMA based simplified Wimax system with and without the application of traffic modeling based policing algorithms. We also evaluate the performance of the proposed algorithm in policing the traffic in an OFDM / TDMA system.

Estimation of backlog and delay in OFDM/TDMA systems with traffic policing using Network Calculus

Achieving quality of service (QoS) in OFDM based networks depends, among other factors, on mechanisms for traffic policing or control. Aggregate traffic in high speed networks can present multifractal characteristics such as bursts over a wide range of time scales, heavy tailed inter-arrival time densities, self-similarity and long-range dependence. These properties are followed by a degradation of quality of service for the traffic flows. In this paper, we firstly propose a policing algorithm based on multifractal traffic modeling, evaluating its performance in comparison to other algorithms. We present a more precise in general envelope modeling process in order to efficiently police the input traffic to the system. Further, we present an approach based on Network Calculus for estimating quality of service parameters, such as mean queue length (backlog) and delay. We analyze these two parameters for an OFDM/TDMA based simplified WiMAX system with and without the application of traffic modeling based policing algorithms. In other words, we propose an approach for evaluating some QoS parameters of an OFDM/TDMA system where the traffic is policed. We also evaluate the performance of the proposed algorithm in policing the traffic in an OFDM/TDMA system.

Development of Multifractal Models for Self-Similar Traffic Flows

Abstract This paper presents a simple technique of multifractal traffic modeling. It proposes a method of fitting model to a given traffic trace. A comparison of simulation results obtained for an exemplary trace, multifractal model and Markov Modulated Poisson Process models has been performed.

Estimation of loss probability and the admissible number of users in a network link considering a lognormal multifractal traffic model with exponential variance

In this work, we propose an analytical expression for estimating the byte loss probability at a single server queue with multifractal traffic arrivals. Initially, we address the theory concerning multifractal processes, especially the Hölder exponents of the multifractal traffic traces. Next we focus our attention on the second order statistics of multifractal traffic processes. More specifically, we assume that an exponential model is adequate for representing the variance of the traffic process under different time scale aggregation. Then, we compare the performance of the proposed approach to other relevant approaches. In addition, based on the results of the analysis, we propose a new admission control strategy that takes into account the multifractal traffic characteristics. We compare the proposed admission control strategy to some other widely used admission control methods. The simulation results show that the proposed loss probability estimation method is accurate, and the proposed admission control strategy is robust and efficient.

Loss probability estimation for OFDM/TDMA-based wireless systems considering a cascade-based multifractal traffic model

In this paper, we analyze the queueing performance in terms of byte loss rate of an orthogonal frequency-division multiplexing/time division multiple access based wireless system taking into account the multifractal behaviour of the wireless traffic flows. To this end, first, we show evidences of multifractal characteristics on wireless traffic traces. These findings motivated us to derive a loss probability equation for wireless traffic flows that can be applied to an orthogonal frequency-division multiplexing/time division multiple access based wireless system. Simulations and comparisons to other methods are carried out to verify the accuracy of the proposed loss probability estimation approach in describing real traffic queueing behaviour. Copyright © 2012 John Wiley & Sons, Ltd.

Loss probability estimation and control for OFDM/TDMA wireless systems considering multifractal traffic characteristics

In this paper, we analyze the queueing performance in terms of loss rate of an OFDM (orthogonal frequency-division multiplexing)/TDMA (time division multiplexing access) based wireless system taking into account the multifractal behavior of the wireless traffic flows. To this end, first, we show evidences of multifractal characteristics on wireless traffic traces. These findings motivated us to propose a traffic policing and control scheme based on a multifractal envelope process in order to maintain the traffic flows well-behaved, i.e., in accordance to the desired QoS parameters. Furthermore, by assuming a multifractal traffic model, we derive a data loss probability equation for wireless traffic flows that was applied to the OFDM/TDMA based wireless system. Simulations and comparisons to other methods were carried out in order to verify the efficiency of the proposed traffic policing scheme as well as of the loss probability estimation approach.

Modelagem de Tráfego de Redes Utilizando Cascata Multifractal Generalizada

In this paper we propose a multifractal traffic model that is based on a multiplicative cascade presenting specific multiplier distributions in each cascade stage. In the proposed model, the multipliers are obtained through the estimate of their probability densities found in real network traffic by using Kernel and Acceptance/Rejection methods. Statistical analysis and queueing behavior study were carried out for the model validation. Furthermore, we verify the model performance in capturing the traffic trace characteristics in comparison to other multifractal models.

Performance Bounds for a Cascade based Multifractal Traffic Model with Generalized Multiplier Distributions

Performance Bounds for a Cascade based Multifractal Traffic Model with Generalized Multiplier Distributions

Book Reviews: A Summary of Multifractal Traffic Modeling

One of the most important discoveries about Internet traffic is the scaling phenomenon. Different from the traditional traffic pattern, primarily the Poisson model, this type of traffic shows a slowly decaying dependence structure and can be approximately characterized by a self-similar process. Multiscale behavior is a more complex version of self-similarity, indicating that the scaling property of the traffic process isn't uniform across multiple time scales and orders of statistics. It's often modeled with the multifractal process. Multifractal Based Network Traffic Modeling provides an accessible but somewhat narrow summary of the research on self-similar and multiscale traffic

Performance analysis of multifractal network traffic

AbstractIn this paper we present some new results in the framework of multifractal performance analysis including both the characterization and modeling of multifractal network traffic, and also the multifractal queueing performance analysis. We first propose a new multifractal traffic model for network traffic based on the combination of a multiplicative cascade with an independent lognormal process. This traffic model is able to provide a very accurate fit to the multifractal characteristics of datatraffic including both the scaling function and the moment factor, but it is also simple enough from a practical point of view having only three parameters. In addition, the model features many important properties observed in datatraffic including long‐range dependence (LRD), multifractality and lognormality. We also present an approximation for the queue tail asymptotics in an infinite capacity single server queue serviced at a constant rate driven by a general multifractal input process. We show that in the special and important case of the monofractal fractional Brownian motion (fBm) input traffic our result gives the well‐known Weibullian tail. We prove that the class of Gaussian processes with scaling properties is in the class of monofractal processes and we derive the related characterization functions. Applying the approximation we provide a new practical method for queueing performance estimation of general multifractal traffic. Finally, we present a practical case study to show the practical application of our framework for measured data traffic and also to validate both our multifractal model and our queueing results. Copyright © 2004 AEI

A new multifractal network traffic model

In this paper, a new multifractal traffic model to capture the multifractal nature of modern Internet traffic was developed. Employing the algorithm of network traffic analysis (binomial inverse cascade process) to analyze the multifractal feature of traffic data and adopting the algorithm of network traffic synthesis (binomial cascade process) to model the network traffic, this approach gave an easy and efficient way to infer the model parameters from the measured traffic traces. Moreover, the traffic was simulated and analyzed using obtained parameters. It was found that the simulated traffic data were in a close fit to the real trace statistics. The analysis results showed that this model could capture the real network traffic very well.

Multiplicative multifractal modelling of long‐range‐dependent network traffic

AbstractWe present a multiplicative multifractal process to model traffic which exhibits long‐range dependence. Using traffic trace data captured by Bellcore from operations across local and wide area networks, we examine the interarrival time series and the packet length sequences. We also model the frame size sequences of VBR video traffic process. We prove a number of properties of multiplicative multifractal processes that are most relevant to their use as traffic models. In particular, we show these processes to characterize effectively the long‐range dependence properties of the measured processes. Furthermore, we consider a single server queueing system which is loaded, on one hand, by the measured processes, and, on the other hand, by our multifractal processes (the latter forming a MFe/MFg/1 queueing system model). In comparing the performance of both systems, we demonstrate our models to effectively track the behaviour exhibited by the system driven by the actual traffic processes. We show the multiplicative multifractal process to be easy to construct. Through parametric dependence on one or two parameters, this model can be calibrated to fit the measured data. We also show that in simulating the packet loss probability, our multifractal traffic model provides a better fit than that obtained by using a fractional Brownian motion model. Copyright © 2001 John Wiley & Sons, Ltd.

Superposition of multiplicative multifractaltraffic processes

Telecommunications network systems have been observed to contain processing and switching nodes that are loaded by highly bursty traffic streams. Such flows have been noted to exhibit long-range-dependent (LRD) properties. The design and sizing of electronic processing and switching systems critically depends on the features of such processes. LRD traffic streams are modelled as a multiplicative multifractal process. It is proven that superposition of a finite number of multiplicative multifractal traffic streams results asymptotically in another multifractal process. Noting that traffic processes driving network nodes result from the superposition of multiple traffic streams at the input ports of the node, this property makes the multiplicative multifractal traffic model a versatile tool for modelling LRD traffic flows at different locations and levels across a network.

Statistical properties of multiplicativemultifractal processes in modellingtelecommunications traffic streams

Telecommunications network systems have been observed to contain processing and switching nodes that are loaded by highly bursty traffic streams. Such flows exhibit long-range-dependent (LRD) properties. The design and sizing of electronic processing and switching systems depend critically on the features of such processes. LRD traffic streams are modelled as a multiplicative multifractal process. Through simulation, it is shown that similar queueing performance results can be simultaneously obtained for a network operating at low, medium, and high utilisation levels when one compares the system-size tail performance of a single server queueing system driven in the first case by the measured traffic and in the second case by a multiplicative multifractal traffic model. A number of the properties of such processes that are most relevant to their use as network traffic models are proved.