Journal of Network and Systems Management

, Volume 26, Issue 4, pp 1058–1078 | Cite as

CCN Energy-Delay Aware Cache Management Using Quantized Hopfield

  • Fereshte Dehghani
  • Naser Movahhedinia


Internet infrastructure is going to be re-designed as a core network layer, shifting from hosts to contents. To this end, content centric networking (CCN) as one of the most effective architectures has been proposed with significant features of in-network caching to open new possibilities for energy efficiency in content dissemination. However in energy-efficient CCN, less popular contents are cached near the origin server, and therefore in delay sensitive applications with less popularity, it leads to dropping delayed chunks, increasing energy waste, and degrading the quality of service (QoS). In the present paper, the energy consumption in CCN while being aware of QoS consideration in terms of imposed delay is minimized. The minimization is performed through integer linear programming by considering most of the energy consuming components. However, since this problem is NP-hard, a quantized Hopfield neural network with an augmented Lagrange multiplier method (MEDCCN-QHN) is proposed to derive the solution. The numerical results show that the MEDCCN-QHN achieves to better delay profile compared to the optimal energy-efficient algorithm, and near-optimal energy consumption. Moreover, the method is fast due to its parallel execution capability.


Delay-energy aware caching policy Energy efficiency Content centric networking Hopfield neural network 


  1. 1.
    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, pp. 1–12. ACM (2009)Google Scholar
  2. 2.
    Fang, C., Yu, F.R., Huang, T., Liu, J., Liu, Y.: A survey of green information-centric networking: research issues and challenges. IEEE Commun. Surv. Tutor. 17(3), 1455–1472 (2015)CrossRefGoogle Scholar
  3. 3.
    Fang, C., Yu, F.R., Huang, T., Liu, J., Liu, Y.: An energy-efficient distributed in-network caching scheme for green content-centric networks. Comput. Netw. 78, 119–129 (2015)CrossRefGoogle Scholar
  4. 4.
    Koponen, T., Chawla, M., Chun, B.G., Ermolinskiy, A., Kim, K.H., Shenker, S., Stoica, I.: A data-oriented (and beyond) network architecture. In: ACM SIGCOMM Computer Communication Review, vol. 37, 181–192. ACM (2007)Google Scholar
  5. 5.
    Ain, M., Trossen, D., Nikander, P., Tarkoma, S., Visala, K., Rimey, K., Burbridge, T., Rajahalme, J., Tuononen, J., Jokela, P., et al.: Architecture definition, component descriptions, and requirements. Deliverable, PSIRP 7th FP EU-funded project (2009)Google Scholar
  6. 6.
    Imai, S., Leibnitz, K., Murata, M.: Energy-aware cache management for content-centric networking. In: 2013 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 1623–1629. IEEE (2013)Google Scholar
  7. 7.
    Raghavan, B., Ma, J.: The energy and emergy of the internet. In: Proceedings of the 10th ACM Workshop on Hot Topics in Networks, p. 9. ACM (2011)Google Scholar
  8. 8.
    Walker, M.: Surfing the Green Wave in Telecom. Ovum-RHK, London (2008)Google Scholar
  9. 9.
    Choi, N., Guan, K., Kilper, D.C., Atkinson, G.: In-network caching effect on optimal energy consumption in content-centric networking. In: 2012 IEEE International Conference on Communications (ICC), pp. 2889–2894. IEEE (2012)Google Scholar
  10. 10.
    Lee, U., Rimac, I., Kilper, D., Hilt, V.: Toward energy-efficient content dissemination. IEEE Netw. 25(2), 14–19 (2011)CrossRefGoogle Scholar
  11. 11.
    Vasilakos, A.V., Li, Z., Simon, G., You, W.: Information centric network: research challenges and opportunities. J. Netw. Comput. Appl. 52, 1–10 (2015)CrossRefGoogle Scholar
  12. 12.
    Guan, K., Atkinson, G., Kilper, D.C., Gulsen, E.: On the energy efficiency of content delivery architectures. In: 2011 IEEE International Conference on Communications Workshops (ICC), pp. 1–6. IEEE (2011)Google Scholar
  13. 13.
    Llorca, J., Tulino, A.M., Guan, K., Esteban, J., Varvello, M., Choi, N., Kilper, D.: Dynamic in-network caching for energy efficient content delivery. In: 2013 Proceedings IEEE INFOCOM, pp. 245–249. IEEE (2013)Google Scholar
  14. 14.
    Roger, B.M.: Game Theory: Analysis of Conflict. Harvard University Press, Cambridge (1991)zbMATHGoogle Scholar
  15. 15.
    Li, C., Liu, W., Wang, L., Li, M., Okamura, K.: Energy-efficient quality of service aware forwarding scheme for content-centric networking. J. Netw. Comput. Appl. 58, 241–254 (2015)CrossRefGoogle Scholar
  16. 16.
    Pham, T.M., Minoux, M., Fdida, S., Pilarski, M.: Optimization of content caching in content-centric network. (2017)
  17. 17.
    Baliga, J., Ayre, R., Hinton, K., Tucker, R.S.: Architectures for energy-efficient IPTV networks. In: Optical Fiber Communication Conference, p. OThQ5. Optical Society of America (2009)Google Scholar
  18. 18.
    Salsano, S., Detti, A., Cancellieri, M., Pomposini, M., Blefari-Melazzi, N.: Transport-layer issues in information centric networks. In: Proceedings of the Second Edition of the ICN Workshop on Information-Centric Networking, pp. 19–24. ACM (2012)Google Scholar
  19. 19.
    Potlapally, N.R., Ravi, S., Raghunathan, A., Jha, N.K.: A study of the energy consumption characteristics of cryptographic algorithms and security protocols. IEEE Trans. Mob. Comput. 5(2), 128–143 (2006)CrossRefGoogle Scholar
  20. 20.
    Kramer, S.: An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J. Mech. Des. 116, 405 (1994)CrossRefGoogle Scholar
  21. 21.
    Wen, U., Lan, K., Shih, H.: A review of hopfield neural networks for solving mathematical programming problems. Eur. J. Oper. Res. 198(3), 675–687 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. 79(8), 2554–2558 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Joya, G., Atencia, M., Sandoval, F.: Hopfield neural networks for optimization: study of the different dynamics. Neurocomputing 43(1), 219–237 (2002)CrossRefzbMATHGoogle Scholar
  24. 24.
    Hopfield, J.J., Tank, D.W.: “Neural” computation of decisions in optimization problems. Biol. Cybern. 52(3), 141–152 (1985)zbMATHGoogle Scholar
  25. 25.
    Matsuda, S.: Quantized hopfield networks for integer programming. Syst. Comput. Jpn. 30(6), 1–12 (1999)CrossRefGoogle Scholar
  26. 26.
    Li, S.Z.: Improving convergence and solution quality of hopfield-type neural networks with augmented Lagrange multipliers. IEEE Trans. Neural Netw. 7(6), 1507–1516 (1996)CrossRefGoogle Scholar
  27. 27.
    Fricker, C., Robert, P., Roberts, J., Sbihi, N.: Impact of traffic mix on caching performance in a content-centric network. In: 2012 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 310–315. IEEE (2012)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Computer EngineeringUniversity of IsfahanIsfahanIran

Personalised recommendations