Abstract
New Internet services like video on-demand, high definition IPTV, high definition video conferences and some real time applications have strong QoS requirements regarding losses, delay, jitter, etc. This work addresses the challenge of guaranteeing quality of service (QoS) in the Internet from a statistical point of view. Three lines of work are proposed. The first one is about the estimation of the QoS parameters from traffic traces (in the context of large deviation theory and effective bandwidth). The second one, address the admission control problem from results of the many sources and small buffer asymptotic. Finally, the third line focuses on the estimation of QoS parameters seen by an application based on end-to-end active measurements and statistical learning tools.
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References
Breslau, L., Jamin, S., Shenker, S.: Comments on the performance of measurement-based admision control algorithm. In: IEEE INFOCOM 2000, Tel Aviv, Israel, pp. 1233–1242 (2000)
Dembo, A., Zeitouni, O.: Large Deviations Techniques and its Applications. Jones and Bartlett, New York (1993)
Kelly, F.: Notes on Effective Bandwidth. In: Kelly, Zachary, Ziedins (eds.) Stochastic Networks: Theory and Applications. Oxford University Press, Oxford (1996)
Ozturk, O., Mazumdar, R., Likhanov, N.: Many sources asymptotics in networks with small buffers. Queueing Systems (QUESTA) 46(1-2), 129–147 (2004)
Más, N., Karlsson, G.: Probe-based admission control for a differentiated-services internet. Computer Networks 51, 3902–3918 (2007)
Wischik, D.: Sample path large deviations for queues with many inputs. Annals of Applied Probability (11), 389–404 (2000)
Courcoubetis, C., Siris, V.A.: Procedures and tools for analysis of network traffic measurements. Elsevier Science, Amsterdam (2001)
Rabinovitch, P.: Statistical estimation of effective bandwidth. M.Sc.thesis, University of Cambridge (2000)
Kesidis, G., Walrand, J., Chang, C.S.: Effective Bandwidths for Multiclass Markov Fluids and Other ATM Sources. IEEE/ACM Trans. Networking (1), 424–428 (1993)
Pechiar, J., Perera, G., Simon, M.: Effective Bandwidth estimation and testing for Markov sources. In: Kouvatsos, D.D. (ed.) Performance Evaluation. Elsevier, New Holland (2002)
Lebedev, E.A., Lukashuk, L.I.: Maximum likelihood estimation of the infinitesimal matrix of a Markov chain with continuous time (Russian, English summary). Dokl. Akad. Nauk Ukr. SSR, Ser. A (1), 12–14 (1986)
Aspirot, L., Belzarena, P., Bermolen, P., Ferragut, A., Perera, G., Simon, M.: Quality of service parameters and link operating point estimation based on effective bandwidths. Performance Evaluation 59(2-3), 103–120 (2005)
Wischik, D.: The output of a switch or effective bandwidths for network. Queueing Systems 32(4), 383–396 (1999)
Belzarena, P., Bermolen, P., Simon, M., Casas, P.: End-to-End Quality of Service-based Admission Control Using the Fictitious Network Analysis. Computer Communications (COMCOM) - The International Journal for the Computer and Telecommunications Industry, Special issue on Heterogeneous Networks: Traffic Engineering and Performance Evaluation (2010) (to appear)
Nadaraya, E.A.: Nonparametric estimation of probability densities and regression curves. Mathematics and its Applications (Soviet Series), vol. 20. Kluwer Academic Publishers Group, Dordrecht (1989)
Ferraty, F., Vieu, P.: Nonparametric Functional data analysis: Theory and Practice. Springer Series in Statistics. Springer, Heidelberg (2006)
Machiraju, S., Veitch, D., Baccelli, F., Nucci, A., Bolot, J.: Theory and practice of cross-traffic estimation. In: SIGMETRICS, pp. 400–401 (2005)
Nadaraya, E.A.: On estimating regression. Theory of Probability and its Applications 9(1), 141–142 (1961)
Masry, E.: Nonparametric regression estimation for dependent functional data: asymptotic normality. Stochastic Process. Appl. 115, 155–177 (2005)
Zhang, Y., Duffield, N.: On the constancy of internet path properties. In: IMW 2001: Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement, pp. 197–211 (2001)
Perera, G.: Irregular sets and central limit theorems. Bernoulli 8, 627–642 (2002)
Bertin, K., Aspirot, L.: Asymptotic normality of the Nadaraya-Watson estimator for non-stationary data. To appear in Journal of Nonparametric Statistics (2009)
Aspirot, L., Belzarena, P., Bazzano, B., Perera, G.: End-To-End Quality of Service Prediction Based On Functional Regression. In: Proc. Third International Working Conference on Performance Modelling and Evaluation of Heterogeneous Networks (HET-NETs 2005), Ilkley, UK (2005)
McCanne, S., Floyd, S.: ns network simulator, http://www.isi.edu/nsnam/ns/ (accessed March 2009)
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Belzarena, P., Simon, M. (2011). The Search for QoS in Data Networks: A Statistical Approach. In: Kouvatsos, D.D. (eds) Network Performance Engineering. Lecture Notes in Computer Science, vol 5233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02742-0_38
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DOI: https://doi.org/10.1007/978-3-642-02742-0_38
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