Cognitive Congestion Control for Data Portals with Variable Link Capacity

Abstract

Network congestion, one of the challenging tasks in communication networks, leads to queuing delays, packet loss, or the blocking of new connections. In this study, a data portal is considered as an application-based network, and a cognitive method is proposed to deal with congestion in this kind of network. Unlike previous methods for congestion control, the proposed method is an effective approach for congestion control when the link capacity and information inquiries are unknown or variable. Using sufficient training samples and the current value of the network parameters, available bandwidth is adjusted to distribute the bandwidth among the active flows. The proposed cognitive method was tested under such situations as unexpected variations in link capacity and oscillatory behavior of the bandwidth. Based on simulation results, the proposed method is capable of adjusting the available bandwidth by tuning the queue length, and provides a stable queue in the network.

Share and Cite:

E. Sharifahmadian and S. Latifi, "Cognitive Congestion Control for Data Portals with Variable Link Capacity," International Journal of Communications, Network and System Sciences, Vol. 5 No. 8, 2012, pp. 481-489. doi: 10.4236/ijcns.2012.58058.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] V. Jacobson, “Congestion Avoidance and Control,” ACM SIGCOMM Computer Communication Review, Vol. 18, No. 4, 1988, pp. 314-329.
[2] J. Postel, “Transmission Control Protocol,” IETF RFC 793, 1981.
[3] L. Xu, K. Harfoush and I. Rhee, “Binary Increase Congestion Control (BIC) for Fast Long-Distance Networks,” INFOCOM 23rd Annual Joint Conference of the IEEE Computer and Communications Societies, Hong Kong, 7-11 March 2004, pp. 2514-2524.
[4] S. Ha, I. Rhee and L. Xu, “CUBIC: A New TCP-Friendly High-Speed TCP Variant,” ACM SIGOPS Operating Systems Review, Vol. 42, No. 5, 2008, pp. 64-74. doi:10.1145/1400097.1400105
[5] S. Floyd, “High Speed TCP for Large Congestion Windows,” RFC3649, ICSI, Berkeley, 2003.
[6] S. Ekelin, M. Nilsson, E. Hartikainen, A. Johnsson, J. E. Mangs, B. Melander and M. Bjorkman, “Real-Time Measurement of End-to-End Available Bandwidth using Kalman Filtering,” Proceedings of the 10th IEEE/IFIP Network Operations and Management Symposium, Vancouver, 3-7 April 2006, pp. 73-84.
[7] J.-C. Bolot, “Characterizing End-to-End Packet Delay and Loss in the Internet,” Journal of High Speed Networks, Vol. 2, No. 3, 1993, pp. 305-323.
[8] S. H. Low, F. Paganini, J. Wang, S. Adlakha and J. C. Doyle, “Dynamics of TCP/AQM and a Scalable Control,” Proceedings of IEEE INFOCOM, New York, 23-27 June 2002, pp. 1-10.
[9] S. Athuraliya, S. H. Low, V. H. Li and Q. Yin, “REM: Active Queue Management,” IEEE Network, Vol. 15, No. 3, 2001, pp. 48-53. doi:10.1109/65.923940
[10] S. Floyd and V. Jacobson, “Random Early Detection Gateways for Congestion Avoidance,” IEEE/ACM Trans- actions on Networking, Vol. 1, No. 4, 1993, pp. 397-413. doi:10.1109/90.251892
[11] C. V. Hollot, V. Misra, D. Towsley and W.-B. Gong, “On Designing Improved Controllers for AQM Routers Supporting TCP Flows,” Proceedings of IEEE INFOCOM, Anchorage, 22-26 April 2001, pp. 1726-1734.
[12] R. J. Gibbens and F. P. Kelly, “Distributed Connection Acceptance Control for a Connectionless Network,” Proceedings of the 16th International Teletraffic Congress, Edinburgh, 7-11 June 1999, pp. 941-952.
[13] F. Abrantes and M. Ricardo, “XCP for Shared-Access Multi-Rate Media,” ACM SIGCOMM Computer Communication Review, Vol. 36, No. 3, 2006, pp. 27-38. doi:10.1145/1140086.1140091
[14] N. Dukkipati, M. Kobayashi, R. Zhang-Shen and N. McKeown, “Processor Sharing Flows in the Internet,” In: H. de Meer and N. Bhatti, Eds., IEEE International Workshop Quality of Service, IFIP International Federation for Information Processing, 2005, pp. 267-281.
[15] A. Afanasyev, N. Tilley, P. Reiher and L. Kleinrock, “Host-to-Host Congestion Control for TCP,” IEEE Communications Surveys & Tutorials, Vol. 12, No. 3, 2010, pp. 304-342. doi:10.1109/SURV.2010.042710.00114
[16] L. Chen, T. Ho, M. Chiang, S. H. Low and J. C. Doyle, “Congestion Control for Multicast Flows with Network Coding,” IEEE Transactions on Information Theory, No. 99, 2012.
[17] B. Wydrowski and M. Zukerman, “MaxNet: A Congestion Control Architecture,” IEEE Communications Letters, Vol. 6, No. 11, 2002, pp. 512-514. doi:10.1109/LCOMM.2002.805519
[18] B. Wydrowski, L. L. H. Andrew and M. Zukerman, “MaxNet: A Congestion Control Architecture for Scalable Networks,” IEEE Communications Letters, Vol. 7, No. 10, 2003, pp. 511-513. doi:10.1109/LCOMM.2003.818888
[19] Y. Zhang and T. Henderson, “An Implementation and Experimental Study of the Explicit Control Protocol (XCP),” Proceedings of the IEEE AVFUCUM, 13-17 March 2005, pp. 1037-1048.
[20] Y. Xia, L. Subramanian, I. Stoica and S. Kalyanaraman, “One More Bit Is Enough,” IEEE/ACM Transactions on Networking, Vol. 16, No. 6, 2008, pp. 1281-1294. doi:10.1109/TNET.2007.912037
[21] V. Misra, W.-B. Gong and D. Towsley, “Fluid-Based Analysis of a Network of AQM Routers Supporting TCP Flows with an Application to RED,” Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, New York, 28 August-1 September 2000, pp. 151-160.
[22] G. Montenegro, S. Dawkins, M. Kojo, V. Magret and N. Vaidya, “Long Thin Networks,” RFC 2757, Texas A & M University, College Station,, 2000.
[23] F. Paganini, J. C. Doyle and S. H. Low, “Scalable Laws for Stable Network Congestion Control,” Proceedings of 40th IEEE Conference on Decision and Control, Orlando, 4-7 December 2001, pp. 185-190.
[24] R. G. M. Morris, L. Tarassenko and M. Kenward, “Cognitive Systems: Information Processing Meets Brain Science,” Academic Press, Cambridge, 2005.
[25] R. E. Neapolitan, “Learning Bayesian Networks,” Prentice Hall, Upper Saddle River, 2004.
[26] N. Christofides, “Graph theory: An Algorithmic Approach,” Academic Press, Cambridge, 1975.
[27] K. Thulasiraman and M. N. S. Swamy, “Graphs: Theory and Algorithms,” John Wiley & Son, Hoboken, 1992.
[28] J. Bang-Jensen and G. Gutin, “Digraphs: Theory, Algorithms and Applications,” 2nd Edition, Springer-Verlag, London, 2008.
[29] C. M. Bishop, “Pattern Recognition and Machine Learning,” Springer, Madison, 2006.
[30] D. Koller and N. Friedman, “Probabilistic Graphical Models: Principles and Techniques,” The MIT Press, Cambridge, 2009.
[31] F. V. Jensen and T. D. Nielsen, “Bayesian Networks and Decision Graphs,” 2nd Edition, Springer Science and Business Media, New York, 2007. doi:10.1007/978-0-387-68282-2
[32] G. Schwarz, “Estimating the Dimension of a Model,” Annals of Statistics, Vol. 6, No. 2, 1978, pp. 461-464. doi:10.1214/aos/1176344136
[33] G. Quer, H. Meenakshisundaram, B. R. Tamma, B. S. Manoj, R. Rao and M. Zorzi, “Using Bayesian Networks for Cognitive Control of Multi-Hop Wireless Networks,” IEEE Military Communications Conference, San Jose, 31 October-3 November 2010, pp. 201-206.
[34] S. J. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach,” 2nd Edition, Prentice Hall, Upper Saddle River, 2003.
[35] T. Ellman, J. Keane and M. Schwabacher, “Intelligent Model Selection for Hill Climbing Search in Computer-Aided Design,” Proceedings of the 11th National Conference on Artificial Intelligence, Washington, 11-15 July 1993, pp. 594-599.
[36] C. Andrieu, N. de Freitas, A. Doucet and M. I. Jordan, “An Introduction to MCMC for Machine Learning,” Machine Learning, Vol. 50, No. 1, 2003, pp. 5-43. doi:10.1023/A:1020281327116
[37] P. Diaconis, “The Markov Chain Monte Carlo Revolution,” Bulletin of the American Mathematical Society, Vol. 46, No. 2, 2009, pp. 179-205. doi:10.1090/S0273-0979-08-01238-X
[38] S. Yang and K.-C. Chang, “Comparison of Score Metrics for Bayesian Network Learning,” IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, Vol. 32, No. 3, 2002, pp. 419-428. doi:10.1109/TSMCA.2002.803772
[39] http://www.nsnam.org/

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.