Flávio Geraldo Coelho Rocha

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.

An adaptive fuzzy model using orthonormal basis functions based on multifractal characteristics applied to network traffic control

In this paper, we present an adaptive predictive orthonormal basis functions (OBF)-fuzzy model that considers the multifractal behavior of network traffic flows. To this end, we model the traffic flows using orthonormal basis functions obtained through multifractal analysis. We insert the orthonormal basis functions into a fuzzy model trained with an adaptive clustering algorithm. Further, we propose a predictive flow control scheme for broadband networks. Also, using the fuzzy model parameters, we derive an expression for the optimal traffic source rate. Comparisons to other predictive control schemes prove the efficiency of the proposed adaptive OBF-fuzzy based control and training algorithm.

Modeling of MPEG-4 Video Traffic Using a Multifractal Cascade with Autoregressive Multipliers

In this paper we propose a video traffic model based on a multifractal multiplicative cascade. In our proposal, we assume that the cascade multipliers can be efficiently described by autoregressive models in each cascade scale. This multiplicative cascade model with autoregressive process based multipliers is used for GOP (Group of Pictures) level traffic modeling as well as for frame level traffic modeling. Aiming to validate the model, we performed statistical and queue behavior analysis of the video traffic processes generated using the proposed model. We also verified the model performance in capturing the real MPEG-4 video traffic characteristics compared to other multifractal models.

ADAPTIVE RATE CONTROL BASED ON LOSS PROBABILITY ESTIMATION CONSIDERING A CASCADE BASED MULTIFRACTAL MODEL FOR THE NETWORK TRAFFIC

In this paper, we propose an expression for estimating the byte loss probability in a single server link taking into account the multifractal properties of network traffic. Using the proposed loss probability estimation approach, we present an adaptive transmission rate control scheme for network communication servers. The proposed algorithm includes a search algorithm in order to find the rate that attains the required loss probability in a network communication link. Finally, we compare the proposed multifractal based adaptive rate allocation to a monofractal based dynamic allocation scheme present in the literature.

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.

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

Summary This paper presents a model based on Queueing Theory for Orthogonal Frequency-Division Multiplexing–Time-Division Multiple Access (OFDM–TDMA) networks. This network model requires a Markovian model to the traffic processes. Thus, we present a Markov Modulated Fluid Model (MMFM) with autocorrelation function fitting for the network traffic, in order to evaluate the performance of the transmission link in OFDM–TDMA systems. That is, we deduce equations for estimating QoS parameters of OFDM–TDMA systems. The obtained results confirm that the proposed network model is efficient in describing the OFDM based link performance. A novel equation is also introduced for estimating the buffer overflow probability in OFDM–TDMA systems using a proposed network service curve. The computational effort is reduced compared to the Queueing Theory based method of overflow probability estimation. Besides, the results show that the estimates are very close to those obtained by simulations.

PREDIÇÃO DE TRÁFEGO DE REDES UTILIZANDO UM MODELO FUZZY COM FUNÇÕES DE BASE ORTONORMAIS BASEADAS EM PROPRIEDADES MULTIFRACTAIS

 Efficient traffic prediction algorithms are important tools to provide resource allocation and congestion control in computer networks. Accordingly, the prediction step can be adjusted in order to enable the network to have time to obtain and allocate resources and thus ensure the desired quality of service. In this article, we present an adaptive predictive model Fuzzy-FBO that considers the multifractal behavior of network traffic flows. To this end, we model the traffic flows using orthonormal basis functions obtained through information from the multifractal analysis of these flows. Then, we insert the orthonormal basis functions into an adaptive fuzzy model trained with an algorithm we call ARFA (Adaptive Regressive Fuzzy Grouping). Comparisons with other predictive schemes prove the efficiency of the proposed model. Keywords Traffic prediction, Multifractal traffic, Orthonormal basis functions, Fuzzy Logic. Resumo Algoritmos eficientes de predição da taxa de tráfego são ferramentas importantes em esquemas para antecipar as ações de alocação de recursos e controle de congestionamento em redes de computadores. Neste sentido, o passo de predição pode ser ajustado de forma a possibilitar que a rede tenha tempo para obtenção e alocação dos recursos necessários e assim garantir a qualidade de serviço desejada. Neste artigo, apresentamos um modelo preditivo adaptativo Fuzzy-FBO que leva em consideração o comportamento multifractal dos fluxos de tráfego de redes. Para tanto, modelamos os fluxos de tráfego através de funções de base ortonormais obtidas através de informações provindas da análise multifractal desses fluxos. Em seguida, inserimos as funções de base ortonormal em um modelo fuzzy adaptativo treinado com um algoritmo que denominamos de ARFA (Agrupamento Regressivo Fuzzy Adaptativo). Comparações com outros esquemas preditivos comprovam a eficiência do modelo proposto. Palavras-chave Predição de Tráfego, Tráfego Multifractal, Funções de Base Ortonormais, Lógica Fuzzy.

Dynamic resource allocation in LTE systems using an algorithm based on Particle Swarm Optimization and βMWM network traffic modeling

In this paper, we propose a resource block allocation scheme for LTE systems (Long Term Evolution) in order to attain QoS requirements based on the effective bandwidth theory. To this end, first we propose an adaptive algorithm to estimate the Multifractal Wavelet Model (MWM) parameters in order to calculate the effective bandwidth for the incoming traffic flows into the system. Next, we apply a PSO (Particle Swarm Optimization) algorithm in order to decide the number of resource blocks to be allocated to each user, providing a minimum rate per user. We validate the efficiency of the proposed adaptive allocation scheme via simulations with real traffic traces.

Dynamic time slot allocation for Multiuser OFDM/TDMA networks using effective bandwidth and βMWM network traffic modeling

In this paper, we propose an adaptive allocation scheme which determines the number of time slots for each user in an OFDM/TDMA system using the Effective Bandwidth theory. For that purpose, we developed an algorithm to adaptively estimate the parameters of the MWM (Multifractal Wavelet Model) traffic model that allow us to calculate the effective bandwidth for the incoming traffic flows. We evaluate the performance of the proposed adaptive allocation scheme through simulations using real traffic traces.

Traffic modeling and analysis of PLC networks using Markov chains

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 traffic and VoIP in PLC networks. Simulations and comparisons were carried out with other models such as Poisson and MMPP (Markov Modulated Poisson Process). The results were obtained by experiments in low-voltage PLC networks presenting bandwidth between 4.3MHz to 20.9 MHz.