A neural network based congestion control algorithm for content-centric networks

  • Authors

    • Parisa Bazmi Shiraz university of technology
    • Manijeh Keshtgary Shiraz university of technology
    2014-10-23
    https://doi.org/10.14419/jacst.v3i2.3696
  • Abstract

    Communication across the Internet has transformed over the years, generated primarily by changes in the importance of content distribution. In the twenty-first century, people are more concerned with the content rather than the location of the information. Content-Centric Networking (CCN) is a new Internet architecture, which aims to access content by a name rather than the IP address of a host. Having the content, CCN which is natively pull-based functions based on the requests received from customers. It is also combined with the availability of in-network chaching. Because of the availability of in-network caching in CCN, chunks may be served by multiple sources. This multi-path transfer in CCN makes TCP-based congestion control mechanisms inefficient for CCN. In this paper a new congestion control algorithm is proposed, which is based on Neural Network prediction over content-centric networks. The designed NN is implemented in each router to predict adaptively the existence of the congestion on link given the current status of the network. The results demonstrate that the proposed congestion control algorithm can effectively improve throughput by 85.53%. This improvement is done by preventing queue overflow from happening, which will result in reductions in packet drop in the network.

    Keywords: Content-Centric Network, Congestion Control, Drop Prediction, Named Data Networking, Neural Network.

  • References

    1. V. Jacobson, D. K. Smetters, J. D. Thornton, M. F. Plass, N. H. Briggs, and R. L. Braynard, "Networking named content," in Proceedings of the 5th international conference on Emerging networking experiments and technologies, 2009, pp. 1-12. http://dx.doi.org/10.1145/1658939.1658941.
    2. Named data networking project (NDN),” Available: http://named-data.org.
    3. M. Särelä, T. Rinta-aho, and S. Tarkoma, "RTFM: Publish/subscribe internetworking architecture," in ICT-MobileSummit Conference Proceedings, 2008.
    4. G. Carofiglio, M. Gallo, and L. Muscariello, "ICP: Design and evaluation of an interest control protocol for content-centric networking," in Computer Communications Workshops (INFOCOM WKSHPS), 2012 IEEE Conference on, 2012, pp. 304-309.
    5. G. Carofiglio, M. Gallo, and L. Muscariello, "Joint hop-by-hop and receiver-driven interest control protocol for content-centric networks," in Proceedings of the second edition of the ICN workshop on Information-centric networking, 2012, pp. 37-42. http://dx.doi.org/10.1145/2342488.2342497.
    6. S. Salsano, A. Detti, M. Cancellieri, M. Pomposini, and N. Blefari-Melazzi, "Transport-layer issues in information centric networks," in Proceedings of the second edition of the ICN workshop on Information-centric networking, 2012, pp. 19-24. http://dx.doi.org/10.1145/2342488.2342493.
    7. S. Arianfar, P. Nikander, L. Eggert, and J. Ott, "Contug: A receiver-driven transport protocol for content-centric networks", in IEEE ICNP 2010 (Poster session).
    8. Y. Wang, T. Muto, Z. Su, J. Katto, and S. Awiphan, "A Consideration on Congestion Control for CCN," Packet Video Workshop, Poster Session, 2013.
    9. G. Carofiglio, M. Gallo, L. Muscariello, and M. Papalini, "Multipath congestion control in content-centric networks," IEEE NOMEN, vol. 13, 2013.
    10. S. Oueslati, J. Roberts, and N. Sbihi, "Flow-aware traffic control for a content-centric network," in INFOCOM, 2012 Proceedings IEEE, 2012, pp. 2417-2425.
    11. C. Yi, A. Afanasyev, I. Moiseenko, L. Wang, B. Zhang, and L. Zhang, "A case for stateful forwarding plane," Computer Communications, vol. 36, pp. 779-791, 2013. http://dx.doi.org/10.1016/j.comcom.2013.01.005.
    12. F. Zhang, Y. Zhang, A. Reznik, H. Liu, C. Qian, and C. Xu, "A Transport Protocol for Content-Centric Networking with Explicit Congestion Control," in Proceedings of The The 23rd International Conference on Computer Communications and Networks (ICCCN), 2014.
    13. M. Barabas, G. Boanea, and V. Dobrota, “Multipath Routing Management using Neural Networks-Based Traffic Prediction,” in The Third International Conference on Emerging Network Intelligence, 2011.
    14. M. Rouhani, M. R. Tanhatalab, and A. Shokohi-Rostami, "Nonlinear Neural Network Congestion Control Based on Genetic Algorithm for TCP/IP Networks," in Computational Intelligence, Communication Systems and Networks (CICSyN), 2010 Second International Conference on, 2010, pp. 1-6.
    15. H. C. Cho, M. S. Fadali, and H. Lee, "Neural network control for TCP network congestion," in American Control Conference, 2005. Proceedings of the 2005, 2005, pp. 3480-3485.
    16. G. A. Rovithakis and C. N. Houmkozlis, "A Neural Network Congestion Control Algorithm for the Internet," in Intelligent Control, 2005. Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation, 2005, pp. 450-455.
    17. J. F. Kurose, Computer networking: a top-down approach featuring the Internet: Pearson Education India, 2005, pp. 271-293.
    18. D.-M. Chiu and R. Jain, "Analysis of the increase and decrease algorithms for congestion avoidance in computer networks," Computer Networks and ISDN systems, vol. 17, pp. 1-14, 1989. http://dx.doi.org/10.1016/0169-7552 (89)90019-6.
    19. V. Jacobson, "Congestion avoidance and control," in ACM SIGCOMM Computer Communication Review, 1988, pp. 314-329. http://dx.doi.org/10.1145/52325.52356.
    20. H. R. Mehrvar and M. R. Soleymani, "Packet loss rate prediction using a universal indicator of traffic," in Proceedings of IEEE International Conference on Communications, vol. 3, 2001, pp. 647–653.
    21. Y. Barve, "Neural Network Approach to the Prediction of Percentage Data Packet Loss for Wireless Sensor Networks," in Proceedings of Southeastern Symposium on System Theory, 2009, pp. 143-150.
    22. V. Kůrková, "Kolmogorov's theorem and multilayer neural networks," Neural networks, vol. 5, pp. 501-506, 1992. http://dx.doi.org/10.1016/0893-6080 (92)90012-8.
    23. H. Demuth, M. Beale, and M. Hagan, "Neural network toolbox user’s guide, The MathWorks," Inc., Natrick, USA, 2009.
    24. Afanasyev, I. Moiseenko, and L. Zhang, "ndnSIM: NDN simulator for NS-3," Tech.Rep, NDN-0005, 2012.
    25. Geant project website. Available: http://www.geant.
    26. M. Tortelli, L. A. Grieco, and G. Boggia, "Performance Assessment of Routing Strategies in Named Data Networking," presented at the IEEE ICNP, 2013.
    27. D. Rossi and G. Rossini, "On sizing CCN content stores by exploiting topological information," in INFOCOM Workshops, 2012, pp. 280-285.
    28. V. Ciancaglini, G. Piro, R. Loti, L. A. Grieco, and L. Liquori, "CCN-TV: a data-centric approach to real-time video services," in Advanced Information Networking and Applications Workshops (WAINA), 2013 27th International Conference on, 2013, pp. 982-989.
    29. R. Chiocchetti, D. Rossi, G. Rossini, G. Carofiglio, and D. Perino, "Exploit the known or explore the unknown? Hamlet-like doubts in icn," in Proceedings of the second edition of the ICN workshop on Information-centric networking, 2012, pp. 7-12. http://dx.doi.org/10.1145/2342488.2342491.
    30. D. Rossi and G. Rossini, "Caching performance of content centric networks under multi-path routing (and more)," Relatóriotécnico, Telecom ParisTech, 2011.
  • Downloads

  • How to Cite

    Bazmi, P., & Keshtgary, M. (2014). A neural network based congestion control algorithm for content-centric networks. Journal of Advanced Computer Science & Technology (JACST), 3(2), 214-220. https://doi.org/10.14419/jacst.v3i2.3696

    Received date: 2014-10-07

    Accepted date: 2014-10-17

    Published date: 2014-10-23