Spatio-Temporal Kronecker Compressive Sensing for Traffic Matrix Recovery

A traffic matrix is generally used by several network management tasks in a data center network, such as traffic engineering and anomaly detection. It gives a flow-level view of the network traffic volume. Despite the explicit importance of the traffic matrix, it is significantly difficult to implement a large-scale measurement to build an absolute traffic matrix. Generally, the traffic matrix obtained by the operators is imperfect, i.e., some traffic data may be lost. Hence, we focus on the problems of recovering these missing traffic data in this paper. To recover these missing traffic data, we propose the spatio-temporal Kronecker compressive sensing method, which draws on Kronecker compressive sensing. In our method, we account for the spatial and temporal properties of the traffic matrix to construct a sparsifying basis that can sparsely represent the traffic matrix. Simultaneously, we consider the low-rank property of the traffic matrix and propose a novel recovery model. We finally assess the estimation error of the proposed method by recovering real traffic

Estimação de probabilidade de transbordo do buffer em redes OFDM-TDMA utilizando cadeias de markov e curva de serviço

Este trabalho apresenta duas abordagens para estimação de probabilidade de transbordo do buffer em redes OFDM-TDMA. A primeira abordagem, se baseia em Cadeias de Markov e em Teoria de Filas para descrever o desempenho do enlace de transmissão em sistemas OFDM-TDMA. A segunda abordagem se baseia em curva de serviço e no conceito de Processo Envelope. Mais especificamente, foi proposta uma equação para estimação de probabilidade de transbordo do buffer em sistemas OFDM-TDMA. Para tal, também deduziu-se uma equação para a curva de serviço de Sistemas OFDM-TDMA. Os resultados obtidos mostram que as estimativas de probabilidade de transbordo baseadas na curva de serviço do sistema se aproximam bem dos resultados da simulação e a complexidade computacional do cálculo necessário para obtê-los é significativamente reduzida em relação ao modelo baseado em Cadeias de Markov171320This paper presents two approaches for estimating buffer overflow probability in OFDM-TDMA networks. The first approach is based on Queuing Theory and Markov Chains and is used to evaluate the performance of the transmission link in OFDM-TDMA systems. The second approach is based on Network Service Curve and Envelope Processes of the traffic flows. More specifically, it is proposed an equation to estimate the probabilityof buffer overflow in OFDM-TDMA systems. To this end, an equation for the Service Curve of the considered system, is also proposed. The results show that the estimates are very close to those obtained by simulations and that the computational complexity to obtain them are significantly reduced due to the absence of matrix computatio

Estimação De Probabilidade De Transbordo Do Buffer Em Redes Ofdm-tdma Utilizando Cadeias De Markov E Curva De Serviço

Este trabalho apresenta duas abordagens para estimação de probabilidade de transbordo do buffer em redes OFDM-TDMA. A primeira abordagem, se baseia em Cadeias de Markov e em Teoria de Filas para descrever o desempenho do enlace de transmissão em sistemas OFDM-TDMA. A segunda abordagem se baseia em curva de serviço e no conceito de Processo Envelope. Mais especificamente, foi proposta uma equação para estimação de probabilidade de transbordo do buffer em sistemas OFDM-TDMA. Para tal, também deduziu-se uma equação para a curva de serviço de Sistemas OFDM-TDMA. Os resultados obtidos mostram que as estimativas de probabilidade de transbordo baseadas na curva de serviço do sistema se aproximam bem dos resultados da simulação e a complexidade computacional do cálculo necessário para obtê-los é significativamente reduzida em relação ao modelo baseado em Cadeias de Markov.17132

Adaptive wavelet-based multifractal model applied to the effective bandwidth estimation of network traffic flows

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)The authors investigate effective bandwidth estimation and Quality of Service (QoS) aware bandwidth provisioning for multifractal network traffic flows. They develop a novel adaptive wavelet-based multifractal model (AWMM) by using properties of the wavelet coefficients of multifractal cascade processes. The proposed AWMM has real-time updating capability and proves to be efficient in capturing multifractal network traffic characteristics. In addition, the authors derive an analytical expression for the effective bandwidth estimation of AWMM traffic flows, capable of being used to meet desired byte loss probabilities. Finally, they present an online effective bandwidth estimation algorithm that is incorporated into an adaptive bandwidth provisioning scheme and comparatively evaluated against some other bandwidth allocation methods.36906919Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)FAPESP [06/60363-6

Accurate Heavy Tail Distribution Approximation For Multifractal Network Traffic

In this paper, we propose the use of a Gaussian mixture model to represent the heavy tail distribution of modern network traffic traces. Another novel contribution of this work is the derivation of a general expression for loss probability estimation in a single server queueing system for traffic traces with multifractal characteristics. The efficiency of this statistical modeling and the accuracy of the estimated loss probabilities are experimentally validated by comparing with other four multifractal based approaches: two of them considering two specific heavy tail distributions (lognormal, Pareto) and the well-known MSQ (Multiscale Queue) and CDTSQ (Critical Dyadic Time-Scale Queue) methods.45317Leland, W., Taqqu, M., Willinger, W., Wilson, D., On The Self-Similar Nature of Ethernet Traffic (1994) IEEE/ACM Transactions on Networking, 2 (1), pp. 1-15. , extended version FebNorros, I., A Storage Model with Self-Similar Input (1994) Queueing, 16, pp. 387-396Park, K., Willinger, W., (2000) Self-Similar Network Traffic and Performance Evaluation, , John Wiley and Sons New YorkRiedi, R.H., Crouse, M.S., Ribeiro, V.J., Baraniuk, R.G., A Multifractal Wavelet Model with Application to Network Traffic (1999) IEEE Transactions on Information Theory. (Special Issue on Multiscale Signal Analysis and Modeling), 45, pp. 992-1018. , AprilVieira, F.H.T., Lee, L.L., Adaptive Wavelet Based Multifractal Model Applied to the Effective Bandwidth Estimation of Network Traffic Flows (2009) IET Communications, pp. 906-919. , JuneKrishna, P.M., Gadre, V.M., Desai, U.B., (2003) Multifractal Based Network Traffic Modeling, , Kluwer Academic Publishers, Boston, MAPeltier, R., Véhel, J.L., (1995) Multifractional Brownian Motion: Definition and Preliminary Results, , Technical Report 2695, INRIAVieira, F.H.T., Bianchi, G.R., Lee, L.L., A Network Traffic Prediction Approach Based on Multifractal Modeling (2010) J. High Speed Netw, 17 (2), pp. 83-96McLachlan, G., (1988) Mixture Models, , Marcel Dekker, New York, NYMartinez, W.L., Martinez, A.R., (2008) Computational Statistics Handbook with Matlab, , Chapman & Hall/CRC, Boca Raton, FloridaFisher, A., Calvet, L., Mandelbrot, B.B., (1997) Multifractality of Deutschmark/US Dollar Exchanges Rates, , Yale UniversitySeuret, S., Gilbert, A.C., Pointwise Hölder Exponent Estimation in Data Network Traffic ITC Specialist Semina, Monterey, 2000Stenico, J.W.G., Lee, L.L., Modelagem de Processos Multifractais Baseada em uma Nova Cascata Conservativa Multiplicativa (2011) XXIX Simpósio Brasileiro de Telecomunicações - SBRT 11, 1, pp. 1-6. , 10/2011, Curitiba, PR, BrasilStenico, J.W.G., Lee, L.L., A New Binomial Conservative Multiplicative Cascade Approach for Network Traffic Modeling 27th IEEE International Conference on Advanced Information Networking and Applications - IEEE AINA 2013Falconer, K., (2003) Fractal Geometry: Mathematical Foundations and Applications, , Second Edition Wiley2 Edition November 17Riedi, R.H., An improved multifractal formalism and self-similar measures (1995) Journal of Mathematical Analysis and Applications, 189, pp. 462-1190Asmussen, S., (2000) Ruin Probabilities, , World Sicientific, SingapuraBenes, V., (1963) General Stochastic Processes in Theory of Queues, , Reading, MA: Addison WesleyStenico, J.W.G., Ling, L.L., A Multifractal Based Dynamic Bandwidth Allocation Approach for Network Traffic Flows IEEE International Conference on Communications (ICC), 23-27 May 2010, pp. 1-6Stenico, J.W.G., Ling, L.L., A Control Admission Scheme for Pareto Arrivals with Multi-Scale Characteristics Proceedings of the International Workshop on Telecommunications - IWT 2011, pp. 220-224. , May - 2011, Rio de Janeiro - BrazilRibeiro, V.J., Riedi, R.H., Crouse, M.S., Baraniuk, R.G., Multiscale Queueing Analysis of Long-Range-Dependent Network Traffic IEEE INFOCOM 2000, pp. 1026-1035. , Tel Aviv, Israelhttp://ita.ee.lbl.gov/html/traces.htmlhttp://www.cs.columbia.edu/~hgs/internet/traces.htmlhttp://crawdad.cs.dartmouth.edu/umd/sigcomm200

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. © 2013 IEEE.11511431155Leland, W., Taqqu, M., Willinger, W., Wilson, D., (1994) On the Self- Similar Nature of Ethernet Traffic (Extended Version), 2 (1), pp. 1-15. , IEEE/ACM Transactions on NetworkingFeldmann, A., Gilbert, A.C., Willinger, W., Data networks as cascades: Investigating the multifractal nature of Internet WAN traffic (1998) Computer Communication Review, 28 (4), pp. 42-55Riedi, R.H., (1999) An Introduction to Multifractals, , in Rice University ECE Technical ReportKolmogorov, A.N., The Local Structure of Turbulence in a Compressible Liquid for Very Large Reynolds numbers (1941) C.R. (Dokl.) Acad. Sci. 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