A Reinforcement Learning Approach to Adaptive Forwarding in Named Data Networking

  • Olumide AkinwandeEmail author
  • Erol Gelenbe
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 935)


This paper addresses Information Centric Networks, and considers in-network caching for Named Data Networking (NDN) architectures. We depart from forwarding algorithms which primarily use links that have been selected by the routing protocol for probing and forwarding, and propose an adaptive forwarding strategy using reinforcement learning with the random neural network (NDNFS-RLRNN), to leverage the routing information and actively seek possible deliveries outside these paths in a controlled way. Our simulations show that NDNFS-RLRNN achieves more efficient delivery performance than a strategy that strictly follows the routing layer or a strategy that retrieves contents from the nearest caches by flooding requests.


  1. 1.
    Xylomenos, G., et al.: A survey of information-centric networking research. IEEE Commun. Surv. Tutor. 16(2), 1024–1049 (2014)CrossRefGoogle Scholar
  2. 2.
    Cisco, V.: Cisco visual networking index: forecast and methodology, 2016–2021 (2017)Google Scholar
  3. 3.
    Zhang, L., et al.: Named data networking. SIGCOMM Comput. Commun. Rev. 44(3), 66–73 (2014)CrossRefGoogle Scholar
  4. 4.
    Lehman, V., et al.: An experimental investigation of hyperbolic routing with a smart forwarding plane in NDN. In: 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS), pp. 1–10 (2016)Google Scholar
  5. 5.
    Jacobson, V., Smetters, D.K., Thornton, J.D., Plass, M.F., Briggs, N.H., Braynard, R.L.: Networking named content. In: Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies, CoNEXT 2009, pp. 1–12. ACM, New York, USA (2009)Google Scholar
  6. 6.
    Afanasyev, A., et al.: NFD developer’s guide. Technical Report NDN-0021, Department of Computer Science, University of California, Los Angeles, USA (2014)Google Scholar
  7. 7.
    Yi, C., Afanasyev, A., Wang, L., Zhang, B., Zhang, L.: Adaptive forwarding in named data networking. SIGCOMM Comput. Commun. Rev. 42(3), 62–67 (2012)CrossRefGoogle Scholar
  8. 8.
    Garcia-Luna-Aceves, J., Mirzazad-Barijough, M.: Enabling correct interest forwarding and retransmissions in a content centric network. In: Proceedings of the Eleventh ACM/IEEE Symposium on Architectures for Networking and Communications Systems, ANCS 2015, pp. 135–146. IEEE Computer Society, Washington, USA (2015)Google Scholar
  9. 9.
    Gelenbe, E.: Self-aware networks: the cognitive packet network and its performance. In: Kounev, S., Kephart, J., Milenkoski, A., Zhu, X. (eds.) Self-Aware Computing Systems, pp. 659–668. Springer, Cham (2017). Scholar
  10. 10.
    Gelenbe, E., Fourneau, J.M.: Random neural networks with multiple classes of signals. Neural Comput. 11(4), 953–963 (1999)CrossRefGoogle Scholar
  11. 11.
    Gelenbe, E.: Steps toward self-aware networks. Commun. ACM 52(7), 66–75 (2009)CrossRefGoogle Scholar
  12. 12.
    Birke, R.: Self-aware computing systems: open challenges and future research directions. In: Kounev, S., Kephart, J., Milenkoski, A., Zhu, X. (eds.) Self-Aware Computing Systems, pp. 709–722. Springer, Cham (2017). Scholar
  13. 13.
    Rossini, G., Rossi, D.: Coupling caching and forwarding: benefits, analysis, and implementation. In: Proceedings of the 1st ACM Conference on Information-Centric Networking, ACM-ICN 2014, pp. 127–136. ACM, New York, USA (2014)Google Scholar
  14. 14.
    Yi, C., Afanasyev, A., Moiseenko, I., Wang, L., Zhang, B., Zhang, L.: A case for stateful forwarding plane. Comput. Commun. 36(7), 779–791 (2013)CrossRefGoogle Scholar
  15. 15.
    Qian, H., Ravindran, R., Wang, G.Q., Medhi, D.: Probability-based adaptive forwarding strategy in named data networking. In: 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), pp. 1094–1101 (2013)Google Scholar
  16. 16.
    Nguyen, D., Fukushima, M., Sugiyama, K., Tagami, A.: Efficient multipath forwarding and congestion control without route-labeling in CCN. In: 2015 IEEE International Conference on Communication Workshop (ICCW), pp. 1533–1538 (2015)Google Scholar
  17. 17.
    Posch, D., Rainer, B., Hellwagner, H.: SAF: stochastic adaptive forwarding in named data networking. IEEE/ACM Trans. Netw. 25(2), 1089–1102 (2017)CrossRefGoogle Scholar
  18. 18.
    Chiocchetti, R., Perino, D., Carofiglio, G., Rossi, D., Rossini, G.: Inform: a dynamic interest forwarding mechanism for information centric networking. In: Proceedings of the 3rd ACM SIGCOMM Workshop on Information-Centric Networking, ICN 2013, pp. 9–14. ACM, New York, USA (2013)Google Scholar
  19. 19.
    Boyan, J.A., Littman, M.L.: Packet routing in dynamically changing networks: a reinforcement learning approach. In: Proceedings of the 6th International Conference on Neural Information Processing Systems, NIPS 1993, pp. 671–678. Morgan Kaufmann Publishers Inc., San Francisco, USA (1993)Google Scholar
  20. 20.
    Gelenbe, S.E.: Cognitive packet network. US Patent No. 6804201 (2004)Google Scholar
  21. 21.
    Gelenbe, E.: Réseaux neuronaux aléatoires stables. Comptes-rendus de l’Académie des Sciences. Série 2, Mécanique, Physique, Chimie, Sciences de l’Univers. Sciences de la Terre 310(3), 177–180 (1990)Google Scholar
  22. 22.
    Halici, U.: Reinforcement learning with internal expectation for the random neural network. Eur. J. Oper. Res. 126(2), 288–307 (2000)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Gelenbe, E., Lent, R.: Power-aware ad hoc cognitive packet networks. Ad Hoc Netw. 2(3), 205–216 (2004)CrossRefGoogle Scholar
  24. 24.
    Sakellari, G.: The cognitive packet network: a survey. Comput. J. 53(3), 268 (2010)CrossRefGoogle Scholar
  25. 25.
    Gelenbe, E., Liu, P., Lainé, J.: Genetic algorithms for route discovery. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 36(6), 1247–1254 (2006)CrossRefGoogle Scholar
  26. 26.
    Wang, L., Gelenbe, E.: Adaptive dispatching of tasks in the cloud. IEEE Trans. Cloud Comput. 6(1), 33–45 (2018)CrossRefGoogle Scholar
  27. 27.
    Mastorakis, S., Afanasyev, A., Moiseenko, I., Zhang, L.: ndnSIM 2: an updated NDN simulator for NS-3. Technical Report NDN-0028, Revision 2, NDN (2016)Google Scholar
  28. 28.
    Knight, S., Nguyen, H., Falkner, N., Bowden, R., Roughan, M.: The internet topology zoo. IEEE J. Sel. Areas Commun. 29(9), 1765–1775 (2011)CrossRefGoogle Scholar
  29. 29.
    Chai, W.K., He, D., Psaras, I., Pavlou, G.: Cache “less for more” in information-centric networks (extended version). Comput. Commun, 36(7), 758–770 (2013)CrossRefGoogle Scholar
  30. 30.
    Psaras, I., Chai, W.K., Pavlou, G.: Probabilistic in-network caching for information-centric networks. In: Proceedings of the Second Edition of the ICN Workshop on Information-Centric Networking, ICN 2012, pp. 55–60. ACM, New York, USA (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Electrical and Electronic EngineeringImperial College LondonLondonUK

Personalised recommendations