Submitted by Marlene Santos (marlene.bc.ufg@gmail.com) on 2014-09-24T21:03:59Z
No. of bitstreams: 2
finalfinal.pdf: 9639130 bytes, checksum: f602829a491b238a34d40c598dc5893a (MD5)
license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2014-09-25T10:32:28Z (GMT) No. of bitstreams: 2
finalfinal.pdf: 9639130 bytes, checksum: f602829a491b238a34d40c598dc5893a (MD5)
license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-09-25T10:32:28Z (GMT). No. of bitstreams: 2
finalfinal.pdf: 9639130 bytes, checksum: f602829a491b238a34d40c598dc5893a (MD5)
license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)
Previous issue date: 2014-06-26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Inthispaperweproposeafuzzymodel,calledFuzzyLMScomAutocorrela¸c˜aoMultifractal,
whose weights are updated according to information from multifractal traffic modeling.
These weights are calculated by incorporating an analytical expression for the autocorrelation function of a multifractal model in the training algorithm of the fuzzy model that is based on the Wiener-Hopf filter. We evaluate the prediction performance of the proposed network traffic prediction algorithm with respect to other predictors. Further, we propose a bandwidth allocation scheme for network traffic based on the fuzzy prediction algorithm. Comparisons with other bandwidth allocation schemes in terms of byte loss rate, link utilization, buffer occupancy and average queue size verifies the efficiency of the proposed scheme. Also, We propose an other adaptive fuzzy algorithm, called Fuzzy-LMS-OBF com alfa adaptivo , for traffic flow control described by theβMWM model. The proposed
algorithm uses Orthonormal Basis Functions (OBF) and its training based on the LMS
algorithm. We also present an expression for the optimal traffic source rate derived from
Fuzzy LMS. Then, we evaluate the performance of the Fuzzy-LMS-OBF com alfa adaptivo
algorithm with respect to other methods. Through simulations, we show that the proposed
control scheme is benefited from the superior performance of the proposed fuzzy algorithm.
Comparisons with other methods in terms of mean and variance of the queue size in the
buffer, Utilization rate of the link, Loss rate and Throughput are presented. / Neste trabalho propomos um modelo fuzzy, nomeado Fuzzy LMS com Autocorrela¸c˜ao Multifractal, cujos pesos s˜ao calculados atrav´es de informa¸c˜oes provindas da an´alise multifractal de s´eries temporais. Esses pesos s˜ao encontrados incorporando uma express˜ao anal´ıtica para a fun¸c˜ao de autocorrela¸c˜ao de um modelo multifractal no algoritmo de treinamento do modelo fuzzy que tem como base o filtro de Wiener-Hopf. Avaliamos ent˜ao o desempenho de predi¸c˜ao de tr´afego de redes do modelo fuzzy proposto adaptativo com
rela¸c˜ao a outros preditores. Em seguida, propomos um esquema de aloca¸c˜ao de banda para tr´afego de redes baseado no algoritmo Fuzzy LMS com Autocorrela¸c˜ao Multifractal. Compara¸c˜oes com outros esquemas de aloca¸c˜ao de banda em termos de taxa de perda de bytes, utiliza¸c˜ao do enlace, ocupa¸c˜ao do buffer e tamanho m´edio da fila comprovam a eficiˆencia do algoritmo no esquema utilizado. Al´em disso, propomos um outro algoritmo fuzzy adaptativo para controle de fluxos de tr´afego que podem ser descritos pelo modelo multifractalβMWM, que chamamos de Fuzzy-LMS-OBF com alfa adaptivo, o qual utiliza
Fun¸c˜oes de Bases Ortonormal (FBO) e tem como base de treinamento, o algoritmo LMS. Propomos tamb´em uma equa¸c˜ao para c´alculo da taxa ´otima de controle derivada do modelo Fuzzy LMS. Em seguida, avaliamos o desempenho do algoritmo de controle adaptativo proposto com rela¸c˜ao a outros m´etodos. Atrav´es de simula¸c˜oes, mostramos que os esquemas de controle e aloca¸c˜ao de taxa se favorecem do desempenho dos algoritmos fuzzy adaptativos propostos. Compara¸c˜oes com outros m´etodos em termos de tamanho m´edio e variˆancia da fila no buffer, Taxa de Utiliza¸c˜ao do enlace e Vaz˜ao s˜ao apresentadas.
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.bc.ufg.br:tede/3164 |
| Date | 26 June 2014 |
| Creators | Cardoso, Alisson Assis |
| Contributors | Vieira, Flávio Henrique Teles, Vieira, Flávio Henrique Teles, Carvalho, Cedric Luiz de, Brito, Leonardo da Cunha |
| Publisher | Universidade Federal de Goiás, Programa de Pós-graduação em Engenharia Elétrica e da Computação (EMC), UFG, Brasil, Escola de Engenharia Elétrica, Mecânica e de Computação - EMC (RG) |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | Portuguese |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Source | reponame:Biblioteca Digital de Teses e Dissertações da UFG, instname:Universidade Federal de Goiás, instacron:UFG |
| Rights | http://creativecommons.org/licenses/by-nc-nd/4.0/, info:eu-repo/semantics/openAccess |
| Relation | -5088589215393046129, 600, 600, 600, 600, -7705723421721944646, -4730207349379833806, 2075167498588264571, AQUINO, V. A.; BARRIA, J. A. Multiresolution fir neural-network-based learning algorithm applied to network traffic prediction. IEEE Transactions on Systems, v. 36, n. 2, p. 208–220, 2006. Citado na p´agina 69. BEZDEK, J. Fuzzy models What are they, and why? Fuzzy Systems, IEEE Transactions on, v. 1, n. 1, p. 1–6, 1993. ISSN 1063-6706. Citado 2 vezes nas p´aginas 17 e 55. CHEN, B.; LIU, X.; TONG, S. Adaptive Fuzzy Output Tracking Control of MIMO Nonlinear Uncertain Systems. Fuzzy Systems, IEEE Transactions on, v. 15, n. 2, p. 287–300, April 2007. Citado na p´agina 78. CHEN, B.-S.; PENG, S.-C.; WANG, K.-C. Traffic modeling, prediction, and congestion control for high-speed networks: a fuzzy ar approach. Fuzzy Systems, IEEE Transactions on, v. 8, n. 5, p. 491–508, 2000. Citado na p´agina 55. CHEN, T. M.; LIU, S. S.; SAMALAM, V. K. The Available Bit Rate Service for Data in ATM Networks. Comm. Mag., v. 34, n. 5, p. 56–58, 63–71, maio 1996. Citado 3 vezes nas p´aginas 79, 80 e 89. CHUI, C. K. An Introduction to Wavelets. San Diego, CA, USA: Academic Press Professional, Inc., 1992. ISBN 0-12-174584-8. Citado na p´agina 22. DANG, T. D.; MOLNAR, S.; MARICZA, I. Capturing the complete characteristics of multifractal network traffic. GLOBECOM, Taipei, Taiwan, Novembro 2002. Citado 2 vezes nas p´aginas 21 e 83. DANG, T. D.; MOLNAR, S.; MARICZA, I. Capturing the Complete Multifractal Characteristics of Network Traffic. In: In Proc., GLOBECOM 2002. [S.l.: s.n.], 2002. Citado na p´agina 21. DANG, T. D.; MOLNAR, S.; MARICZA, I. Queuing performance estimation for general multifractal traffic. Int. J. Commun. Syst., v. 16, n. 2, p. 117–136, 2003. Citado 4 vezes nas p´aginas 19, 21, 22 e 28. DEC. Digital Equipament Corporation Traces. 1995. Dispon´ıvel em:<http: //ita.ee.lbl.gov/html/contrib/DEC-PKT.html>. Citado 2 vezes nas p´aginas 57 e 127. DINIZ, P. S. R. Adaptive Filtering: Algorithms and Practical Implementation. [S.l.]: Springer, 2008. Citado 3 vezes nas p´aginas 45, 49 e 54. DITZE, M.; JAHNICH, I. Towards end-to-end QoS in service oriented architectures. In: Industrial Informatics, 2005. INDIN ’05. 2005 3rd IEEE International Conference on. [S.l.: s.n.], 2005. p. 92–97. Citado na p´agina 17. DURRESI, A.; SRIDHARAN, M.; JAIN, R. Congestion control using adaptive multilevel early congestion notification. International Journal of High performance and Networking, v. 4, n. 5, 2006. Citado na p´agina 78. EHLERS, R. S. [S.l.]: Departamento de Estat´ısticas UFPR, 2005. Citado na p´agina 31. FARHANG-BOROUJENY, B. Adaptive Filters: Theory and Applications. [S.l.]: John Wiley & Sons, 1999. Citado 2 vezes nas p´aginas 60 e 90. FELDMANN, A.; GILBERT, A. C.; WILLINGER, W. Data networks as cascades: investigating the multifractal nature of Internet WAN traffic. SIGCOMM Comput. Commun. Rev., ACM, New York, NY, USA, v. 28, n. 4, p. 42–55, 1998. Citado na p´agina 19. GILL, P. E.; MURRAY, W.; WRIGHT, M. H. Practical Optimization. [S.l.]: Emerald Group Publishing Limited, 1982. Citado na p´agina 48. GONCALVES, B. H. P.; VIEIRA, F. H. T.; COSTA, V. H. T. Modelagem Multifractal BMWM Adaptiva para Tr´afego de Redes de Computadores. In: X Encontro Anual de Computa¸c˜ao - EnAComp 2013. [S.l.: s.n.], 2013. p. 383–390. Citado 6 vezes nas p´aginas 24, 25, 82, 83, 85 e 86. GRIPENBERG, G.; NORROS, I. On the prediction of fractional brownian motion. Journal of Applied Probability, v. 33, p. 400–410, 1996. Citado na p´agina 56. GULER, O. Foundations of Optimization (Graduate Texts in Mathematics, Vol. 258). [S.l.]: Springer, 2010. Citado na p´agina 48. HABIB, I. W.; SAADAWI, T. N. Access flow control algorithms in broadband networks. Computer Communications, v. 15, n. 5, p. 326–332, 1992. Citado 2 vezes nas p´aginas 79 e 89. HATANO, T.; SHIGENO, H.; OKADA, K. TCP-friendly Congestion Control for HighSpeed Network. In: Applications and the Internet, 2007. SAINT 2007. International Symposium on. [S.l.: s.n.], 2007. p. 10–10. Citado na p´agina 17. HAYKIN, S. S. Modern filters. [S.l.]: Macmillan New York, 1989. Citado na p´agina 57. HIRCHOREN, G.; ARANTES, D. Predictors for the discrete time fractional gaussian processes. In: Telecommunications Symposium, 1998. ITS ’98 Proceedings. SBT/IEEE International. [S.l.: s.n.], 1998. p. 49–53 vol.1. Citado na p´agina 56. HU, Q.; PETR, D. A predictive self-tuning fuzzy-logic feedback rate controller. Networking, IEEE/ACM Transactions on, v. 8, n. 6, p. 697–709, Dec 2000. Citado na p´agina 78. INTEL. Intel Pentium Processor T4500. 2014. Dispon´ıvel em:<http://ark. intel.com/PT-BR/products/42925/Intel-Pentium-Processor-T4500-1M-Cache-2 30-GHz-800-MHz-FSBf>. Citado na p´agina 58. JACOBSON, V. Congestion Avoidance and Control. SIGCOMM Comput. Commun. Rev., v. 25, n. 1, p. 157–187, jan. 1995. ISSN 0146-4833. Citado na p´agina 81. JANTZEN, J. Foundations of Fuzzy Control. [S.l.]: John Wiley & Sons, 2007. Citado na p´agina 38. JUSAK, J.; HARRIS, R. Study of UDP-based Internet traffic: Long-range dependence characteristics. In: Australasian Telecommunication Networks and Applications Conference (ATNAC), 2011. [S.l.: s.n.], 2011. p. 1–7. ISSN Pending. Citado na p´agina 17. KARNIK, A.; KUMAR, A. Performance of TCP Congestion Control with Explicit Rate Feedback. IEEE/ACM Trans. Netw., IEEE Press, v. 13, n. 1, p. 108–120, fev. 2005. ISSN 1063-6692. Citado 2 vezes nas p´aginas 79 e 89. KIM, E. et al. A new approach to fuzzy modeling. Fuzzy Systems, IEEE Transactions on, v. 5, n. 3, p. 328–337, Aug 1997. Citado na p´agina 55. KIM, S. et al. Dataset of BitTorrent traffic on Korea Telecom’s mobile WiMAX network. 2012. Dispon´ıvel em:<http://crawdad.cs.dartmouth.edu/snu/bittorrent/>. Citado 4 vezes nas p´aginas 22, 88, 129 e 131. KUO, S. M.; LEE, B. H.; TIAN, W. Real-Time Digital Signal Processing: Fundamentals, Implementations and Applications. [S.l.]: John Wiley & Sons, 2013. Citado na p´agina 50. LEE, I. W. C.; FAPOJUWO, A. O. Stochastic Processes for Computer Network Traffic Modeling. Comput. Commun., Elsevier Science Publishers B. V., v. 29, n. 1, p. 1–23, dez. 2005. ISSN 0140-3664. Citado na p´agina 78. LEE, K. Y. Complex fuzzy adaptive filter with LMS algorithm. Signal Processing, IEEE Transactions on, v. 44, n. 2, p. 424–427, 1996. ISSN 1053-587X. Citado na p´agina 57. LEE, T.-J.; VECIANA, G. de. Model and performance evaluation for multiservice network link supporting ABR and CBR services. Communications Letters, IEEE, v. 4, n. 11, p. 375–377, Nov 2000. Citado na p´agina 80. LI, H.; CHEN, C. P.; HUANG, H.-P. Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Applications in Engineering. [S.l.]: CRC Press, 2000. Citado na p´agina 35. LILLY, J. H. Fuzzy control and identification. [S.l.]: John Wiley & Sons, 2010. Citado na p´agina 40. LIU, H.-H.; HSU, P.-L. Design and simulation of adaptive fuzzy control on the traffic network. In: SICE-ICASE, 2006. International Joint Conference. [S.l.: s.n.], 2006. p. 4961–4966. Citado na p´agina 55. MAMDANI, E.; ASSILIAN, S. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, v. 7, n. 1, p. 1 – 13, 1975. Citado na p´agina 35. NORROS, I. A storage model with self-similar input. Queueing Systems. Citado na p´agina 32. OUYANG, C.-S.; LEE, W.-J.; LEE, S.-J. A TSK-type neurofuzzy network approach to system modeling problems. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, v. 35, n. 4, p. 751–767, Aug 2005. Citado na p´agina 78. OUYANG, Y. C.; YANG, C. W.; LIAN, W. S. Neural networks based variable bit rate traffic prediction for traffic control using multiple leaky bucket. Journal of High Speed Networks, v. 15, n. 2, p. 11–122, 2006. Citado na p´agina 69. PAPOULIS, A. Probability, Random Variables, and Stochastic Processes. [S.l.]: Mc-Graw Hill, 1984. Citado na p´agina 58. PARK, K.; WILLINGER, W. Self-similar Network Traffic and Performance Evaluation. New York: John Wiley and Sons, 2000. Citado 4 vezes nas p´aginas 19, 20, 31 e 55. PAVLOV, A. N.; ANISHCHENKO, V. S. Multifractal analysis of complex signals. Physics-Uspekhi, v. 50, n. 8, p. 819, 2007. Citado na p´agina 32. PAXSON, V.; FLOYD, S. Wide area traffic: the failure of poisson modeling. Networking, IEEE/ACM Transactions on, v. 3, n. 3, p. 226–244, Jun 1995. Citado na p´agina 57. RAMAKRISHNAN, K. K.; JAIN, R. A Binary Feedback Scheme for Congestion Avoidance in Computer Networks. ACM Trans. Comput. Syst., v. 8, n. 2, p. 158–181, maio 1990. ISSN 0734-2071. Citado na p´agina 89. RIBEIRO, V. J. et al. Multiscale queuing analysis of long-range-dependent network traffic. In: Proc. IEEE INFOCOM. [S.l.: s.n.], 2000. p. 1026–1035. Citado na p´agina 22. RIBEIRO, V. J. et al. Small-time scaling behavior of internet backbone traffic. Computer Networks: The International Journal of Computer and Telecommunications Networking, Elsevier North-Holland, Inc., v. 48, n. 3, p. 315–334, 2005. Citado na p´agina 31. RIEDI, R. H. et al. A multifractal wavelet model with application to network traffic. Information Theory, IEEE Transactions on, v. 45, n. 3, p. 992–1018, Apr 1999. Citado 5 vezes nas p´aginas 19, 22, 24, 31 e 83. ROCHA, F. G. C.; VIEIRA, F. H. T. Modelagem de tr´afego de v´ıdeo mpeg-4 utilizando cascata multifractal com distribuicao autorregressiva dos multiplicadores. In: The 8th International Information and Telecommunication Technologies Symposium. [S.l.: s.n.], 2009. Citado na p´agina 22. ROLLS, D. A.; MICHAILIDIS, G.; HERN´aNDEZ-CAMPOS, F. Queueing analysis of network traffic: Methodology and visualization tools. Comput. Netw., v. 48, n. 3, p. 447–473, jun. 2005. ISSN 1389-1286. Citado 2 vezes nas p´aginas 65 e 93. RONGCAI, Z.; SHUO, Z. Network traffic generation: A combination of stochastic and self-similar. In: Advanced Computer Control (ICACC), 2010 2nd International Conference on. [S.l.: s.n.], 2010. v. 2, p. 171–175. Citado na p´agina 17. ROSS, T. J. Fuzzy logic with engineering applications. [S.l.]: John Wiley & Sons, 2009. Citado 8 vezes nas p´aginas 6, 17, 35, 36, 40, 42, 43 e 55. SCHILLING, R.; HARRIS, S. Fundamentals of digital signal processing using MATLAB. [S.l.]: Cengage Learning, 2011. Citado na p´agina 45. SEURET, S.; GILBERT, A. Pointwise h¨older exponent estimation in data network traffic. ITC Specialist Semina, 2000. Citado na p´agina 31. SOUZA, B. V. L.; VIEIRA, F. H. T. Algoritmo de predi¸c˜ao de tr´afego de rede baseado na fun¸c˜ao autocorrela¸c˜ao de um modelo multifractal. In: XXXIV Congresso Nacional de Matem´atica Aplicada e Computacional. ´Aguas de Lid´oias, SP: [s.n.], 2012. Citado na p´agina 56. SUGENO, M.; YASUKAWA, T. A fuzzy-logic-based approach to qualitative modeling. Fuzzy Systems, IEEE Transactions on, v. 1, n. 1, p. 7–, Feb 1993. Citado na p´agina 55. TAKAGI, T.; SUGENO, M. Fuzzy identification of systems and its applications to modeling and control. Systems, Man and Cybernetics, IEEE Transactions on, SMC-15, n. 1, p. 116–132, Jan 1985. Citado na p´agina 55. TOREYIN, B. et al. Lms based adaptive prediction for scalable video coding. In: Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on. [S.l.: s.n.], 2006. v. 2, p. II–II. Citado na p´agina 57. TRAN, H. T.; ZIEGLER, T. Adaptive bandwidth provisioning with explicit respect to qos requirements. Computer Communications, v. 28, p. 1862–1876, 2005. Citado na p´agina 69. VIEIRA, F.; ROCHA, F. An adaptive fuzzy model using orthonormal basis functions based on multifractal characteristics applied to network traffic control. Neurocomputing, v. 74, n. 11, p. 1894–1907, 2011. Citado na p´agina 82. VIEIRA, F. H. T.; COSTA, V. H. T.; SOUZA, B. V. L. Neural network based approaches for network traffic prediction. Artificial Intelligence, Evolutionary Computing and Metaheuristics, v. 427, p. 657–684, 2013. Citado na p´agina 22. VIEIRA, F. H. T.; LING, L. L. Modelagem fuzzy utilizando fun¸c˜oes de base ortonormais aplicada `a predi¸c˜ao adaptativa de tr´afego de redes. Learning and Nonlinear Models. Rev. Socied. Brasileira de Redes Neurais (SBRN), v. 4, n. 2, p. 93–11, 2006. Citado 2 vezes nas p´aginas 19 e 26. VIEIRA, F. H. T.; LING, L. L. Performance bounds for a cascade based multifractal traffic model with generalized multiplier distributions. Journal of Communication and Information Systems, v. 21, p. 165–175, 2006. Citado na p´agina 20. VIEIRA, F. H. T.; LING, L. L. Modelagem de tr´afego de redes utilizando cascata multifractal generalizada. Revista de Inform´atica Te´orica e aplicada, v. 15, n. 2, 2008. Citado na p´agina 31. VIEIRA, F. H. T.; ROCHA, F. G. C.; LEMOS, R. P. An algorithm for adaptive prediction of local singularities of network traffic flows. 2010. Citado na p´agina 31. WANG, C. et al. LRED: A Robust and Responsive AQM Algorithm Using Packet Loss Ratio Measurement. Parallel and Distributed Systems, IEEE Transactions on, v. 18, n. 1, p. 29–43, Jan 2007. Citado 2 vezes nas p´aginas 78 e 79. WANG, L.-X. Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 1994. ISBN 0-13-099631-9. Citado 11 vezes nas p´aginas 6, 37, 39, 40, 41, 42, 44, 50, 57, 59 e 88. WANG, Z.-x. et al. Research on fuzzy neural network algorithms for nonlinear network traffic predicting. In: Optoelectronics Letters. [S.l.: s.n.], 2006. p. 373–375. ISSN Pending. Citado na p´agina 17. WITS. WITS: Waikato Internet Traffic Storage from University of Waikato. 2014. Dispon´ıvel em:<http://wand.net.nz/wits/waikato/8/>. Citado 2 vezes nas p´aginas 57 e 88. YU, Y. et al. Traffic prediction in 3G mobile networks based on multifractal exploration. Tsinghua Science and Technology, v. 18, n. 4, p. 398–405, 2013. Citado na p´agina 17. ZHANG, R.; PHILLIS, Y. A.; KOUIKOGLOU, V. S. Fuzzy Control of Queuing Systems. [S.l.]: Springer, 2005. Citado na p´agina 17. ZOU, D.; ZHANG, X.; WANG, W. Multi-service traffic models of heterogeneous wireless communication networks. Proc. of the 7th World Congress on Intelligent Control and Automation, p. 495–498, 2008. Citado na p´agina 21. |
Page generated in 0.0106 seconds