Jeferson Wilian de Godoy Stênico

Modern network traffic modeling based on binomial multiplicative cascades

In this paper we present a new multifractal approach for modern network traffic modeling. The proposed method is based on a novel construction scheme of conservative multiplicative cascades. We show that the proposed model can faithfully capture some main characteristics (scaling function and moment factor) of multifractal processes. For this new network traffic model, we also explicitly derive analytical expressions for the mean and variance of the corresponding network traffic process and show that its autocorrelation function exhibits long-range dependent characteristics. Finally, we evaluate the performance of our model by testing both real wired and wireless traffic traces, comparing the obtained results with those provided by other well-known traffic models reported in the literature. We found that the proposed model is simple and capable of accurately representing network traffic traces with multifractal characteristics.

General solution to the losses estimation for multifractal traffic

Abstract In this work, we present a new approach for loss probability estimation in a single server link. We show how to get the estimates analytically once we assume multifractal input traffic. In order to make the estimation procedure numerically tractable without losing the accuracy, we propose the use of a Gaussian mixture model to represent the heavy tail distribution of modern network traffic trace. The adopted evaluation procedure is based on two performance measures: empirical traffic arrival load distribution and loss probability at connection. Extensive experimental tests validate the efficiency and accuracy of the proposed loss probability estimation approach against the results obtained by simulations with real traffic and by comparing with other multifractal approaches suggested in the literature.

Queuing Modeling Applied to Admission Control of Network Traffic Flows Considering Multifractal Characteristics

In this paper, 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 for 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 with some 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 with 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.

Quality of Service Provision and Data Security in Communication Networks Based on Traffic Classification

In this paper we present a new approach to admission control of new flows in communications networks. Initially, we introduce a traffic classification procedure based on a new construction approach of conservative multiplicative cascades, to ensure information security by blocking malicious attempts and creating priorities over well-intentioned traffic. The admission control process accepts or rejects then the request for a new call by evaluating the available bandwidth within a particular transmission interval for different pre-classified traffic types. The experimental investigation by simulation showed that the proposed method is capable of ensuring the efficient use of available network resources and at the same time providing consistent QoS provisions and data security for network traffic flows.

A New Approach for Buffer Queueing Evaluation Under Network Flows with Multi-scale Characteristics

In this paper, we propose a new analytical expression for estimating byte loss probability at a single server queue with multi-scale traffic arrivals. In order to make the estimation procedure numerically tractable without losing the accuracy, we assume and demonstrate that an exponential model is adequate for representing the relation between mean square and variance of Pareto distributed traffic processes under different time scale aggregation. Extensive experimental tests validate the efficiency and accuracy of the proposed loss probability estimation approach and its superior performance for applications in network connection with respect to some well-known approaches suggested in the literature.

A New Network Traffic Modeling Based in Conservative Multiplicative Cascade with Weights Newton Binomial

In order to robustly characterize todays network traffic, a new multifractal theory based multiplicative cascade traffic model is proposed. The proposed multiplicative cascade traffic model is conservative in measure with its multiplier weights determined by Binomial Newton expansions. We also derive some major statistical characteristics of this new cascade process enabling generation of synthetic traffic used for traffic model validation and simulation purposes. Experimental investigation results have shown that the proposed model is promising in robust and accurate traffic characterization and modeling for both real wired and wireless traffic outperforming three well-known multifractal models in the literature.

A multi-scaling based admission control approach for network traffic flows

In this paper, we propose an analytical expression for estimating byte loss probability at a single server queue with multi-scale traffic arrivals. We extend our investigation on the application potentiality of the estimation method and possible its quality in connection admission control mechanisms. Extensive experimental tests validate the efficiency and accuracy of the proposed loss probability estimation approach and its superior performance for admission control applications in network connection with respect to some well-known approaches suggested in the literature.