Multimedia Systems

, Volume 23, Issue 4, pp 405–419 | Cite as

Management of virtual network resources for multimedia applications

  • R. L. GomesEmail author
  • L. Bittencourt
  • E. Madeira
  • E. Cerqueira
  • M. Gerla
Regular Paper


The Internet is the primary means for multimedia content sharing, playing a central role in the lifestyle of users. As a consequence, in the past few years, the traffic demand for access and edge networks has increased (video stream downloading, videoconferencing or even the broadcasting of video streams through the Internet), since multimedia applications have strict requirements, including high bandwidth, small amount of loss and low delay. To address this scenario, the edge as a service (EaaS) paradigm arises as a suitable approach to increasing the quality of Internet access. The EaaS uses network virtualization and software-defined networks to improve the resource utilization and manageability. Within this context, this article proposes a framework to manage the virtual network resource (VNR) according to the multimedia application characteristics, and not only the network requirements. Additionally, a study about the relationship between quality of experience (QoE) and VNR availability was performed to be used as a basis for a proposed resource allocation adjustment mechanism. Experiments using real multimedia traffic under distinct scenarios demonstrate the effectiveness of the proposed framework to ensure the QoE of users through the management of the VNR.


Real-time adaptation Resource allocation Network virtualization Software-defined networks Quality of experience 



The authors would like to thank São Paulo Research Foundation (FAPESP - Grant 2012/04945-7), CAPES (Grant 12342/13-0), FAPESPA and CNPq for the financial support.


  1. 1.
    Abdeljaouad, I., Karmouch, A.: Utility function for predicting iptv quality of experience based on delay in overlay networks. In: Consumer Communications and Networking Conference (CCNC), 2013 IEEE, pp. 190–195 (2013)Google Scholar
  2. 2.
    Aguiar, E., Riker, A., Cerqueira, E., Abelm, A., Mu, M., Braun, T., Curado, M., Zeadally, S.: A real-time video quality estimator for emerging wireless multimedia systems. Wirel. Netw. 20, 1759–1776 (2014)Google Scholar
  3. 3.
    Davy, S., Famaey, J., Serrat-Fernandez, J., Gorricho, J., Miron, A., Dramitinos, M., Neves, P., Latre, S., Goshen, E.: Challenges to support edge-as-a-service. IEEE Commun. Mag. 52(1), 132–139 (2014)CrossRefGoogle Scholar
  4. 4.
    Drutskoy, D., Keller, E., Rexford, J.: Scalable network virtualization in software-defined networks. IEEE Internet Comput. 17(2), 20–27 (2013)Google Scholar
  5. 5.
    Georgopoulos, P., Elkhatib, Y., Broadbent, M., Mu, M., Race, N.: Towards network-wide qoe fairness using openflow-assisted adaptive video streaming. In: Proceedings of the 2013 ACM SIGCOMM Workshop on Future Human-Centric Multimedia Networking, pp. 15–20. ACM (2013)Google Scholar
  6. 6.
    Gomes, R.L., Bittencourt, L.F., Madeira, E.: A framework for sla establishment of virtual networks based on qos classes. In: Proceedings of Fifth International Workshop on Management of the Future Internet (ManFI) (2013)Google Scholar
  7. 7.
    Gomes, R.L., Bittencourt, L.F., Madeira, E.R.M.: A bandwidth-feasibility algorithm for reliable virtual network allocation. In: 28th IEEE International Conference on Advanced Information Networking and Applications (AINA) (2014)Google Scholar
  8. 8.
    Gomez, G., de Torres, E., Lorca, J., Garca, R., Prez, Q., Arias, E.: Assessment of multimedia services qos/qoe over lte networks. In: Obaidat, Mohammad, S., Filipe, Joaquim (eds.) E-Business and Telecommunications. Communications in Computer and Information Science, vol. 455, pp. 257–272. Springer, Berlin (2014)Google Scholar
  9. 9.
    Grimaudo, L., Mellia, M., Baralis, E.: Hierarchical learning for fine grained internet traffic classification. In: 8th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 463–468 (2012)Google Scholar
  10. 10.
    Harrell, F.: Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Graduate Texts in Mathematics. Springer, Berlin (2001)CrossRefzbMATHGoogle Scholar
  11. 11.
    Hemmati, M., Yassine, A., Shirmohammadi, S.: An online learning approach to qoe-fair distributed rate allocation in multi-user video streaming. In: 8th International Conference on Signal Processing and Communication Systems (ICSPCS), pp. 1–6 (2014)Google Scholar
  12. 12.
    Hirvonen, M., Laulajainen, J.: Two-phased network traffic classification method for quality of service management. In: IEEE 13th International Symposium on Consumer Electronics, 2009 (ISCE ’09), pp. 962–966 (2009)Google Scholar
  13. 13.
    Hobfeld, T., Binzenhofer, A.: Analysis of skype voip traffic in umts: end-to-end qos and qoe measurements. Comput. Netw. 52(3), 650–666 (2008)CrossRefzbMATHGoogle Scholar
  14. 14.
    Hobfeld, T., Tran-Gia, P., Fiedler, M.: Quantification of quality of experience for edge-based applications. In: Mason, L., Drwiega, T., Yan, J. (eds.) Managing Traffic Performance in Converged Networks. Lecture Notes in Computer Science, vol. 4516, pp. 361–373. Springer, Berlin (2007)CrossRefGoogle Scholar
  15. 15.
    ITU-T Recommendation P.910: Subjective video quality assessment methods for multimedia applications (2008)Google Scholar
  16. 16.
    Jin, Y., Duffield, N., Erman, J., Haffner, P., Sen, S., Zhang, Z.L.: A modular machine learning system for flow-level traffic classification in large networks. ACM Trans. Knowl. Discov. Data 6(1), 1–34 (2012)Google Scholar
  17. 17.
    Kim, H., Feamster, N.: Improving network management with software defined networking. IEEE Commun. Mag. 51(2), 114–119 (2013)CrossRefGoogle Scholar
  18. 18.
    Klein, D., Zinner, T., Borchert, K., Lange, S., Singeorzan, V., Schmid, M.: Evaluation of video quality monitoring based on pre-computed frame distortions. In: Advances in Communication Networking. Lecture Notes in Computer Science, vol. 8115 (2013)Google Scholar
  19. 19.
    Lantz, B., Heller, B., McKeown, N.: A network in a laptop: rapid prototyping for software-defined networks. In: Proceedings of the Ninth ACM SIGCOMM Workshop on Hot Topics in Networks, Hotnets ’10, pp. 19:1–19:6. ACM, New York, NY (2010)Google Scholar
  20. 20.
    Mahimkar, A., Chiu, A., Doverspike, R., Feuer, M.D., Magill, P., Mavrogiorgis, E., Pastor, J., Woodward, S.L., Yates, J.: Bandwidth on demand for inter-data center communication. In: 10th ACM Workshop on Hot Topics in Networks (2011)Google Scholar
  21. 21.
    Martinez-Yelmo, I., Seoane, I., Guerrero, C.: Fair quality of experience (qoe) measurements related with networking technologies. In: Osipov, E., Kassler, A., Bohnert, T., Masip-Bruin, X. (eds.) Wired/Wireless Internet Communications. Lecture Notes in Computer Science, vol. 6074, pp. 228–239. Springer, Berlin (2010)CrossRefGoogle Scholar
  22. 22.
    Menkovski, V., Exarchakos, G., Liotta, A.: Online qoe prediction. In: Second International Workshop on Quality of Multimedia Experience (QoMEX), pp. 118–123 (2010)Google Scholar
  23. 23.
    Möller, S., Raake, A.: Quality of Experience: Advanced Concepts. Applications and Methods. T-Labs Series in Telecommunication Services. Springer International Publishing, Cham (2014)CrossRefGoogle Scholar
  24. 24.
    Park, J., Seshadrinathan, K., Lee, S., Bovik, A.: Video quality pooling adaptive to perceptual distortion severity. IEEE Trans. Image Process. 22(2), 610–620 (2013)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Reichl, P., Egger, S., Schatz, R., D’Alconzo, A.: The logarithmic nature of qoe and the role of the weber-fechner law in qoe assessment. In: IEEE International Conference on Communications (ICC) (2010)Google Scholar
  26. 26.
    Serral-Gracià, R., Cerqueira, E., Curado, M., Yannuzzi, M., Monteiro, E., Masip-Bruin, X.: An overview of quality of experience measurement challenges for video applications in ip networks. In: Proceedings of the 8th International Conference on Wired/Wireless Internet Communications, pp. 252–263. Springer, Berlin (2010)Google Scholar
  27. 27.
    Shafiq, M.Z., Ji, L., Liu, A.X., Wang, J.: Characterizing and modeling internet traffic dynamics of cellular devices. In: Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS ’11, pp. 305–316. ACM (2011)Google Scholar
  28. 28.
    Sherwood, R., Chan, M., Covington, A., Gibb, G., Flajslik, M., Handigol, N., Huang, T.Y., Kazemian, P., Kobayashi, M., Naous, J., Seetharaman, S., Underhill, D., Yabe, T., Yap, K.K., Yiakoumis, Y., Zeng, H., Appenzeller, G., Johari, R., McKeown, N., Parulkar, G.: Carving research slices out of your production networks with openflow. SIGCOMM Comput. Commun. Rev. 40, 129–130 (2010)CrossRefGoogle Scholar
  29. 29.
    Skoldstrom, P., Yedavalli, K.: Network virtualization and resource allocation in openflow-based wide area networks. In: Proceedings of IEEE International Conference on Communications (ICC) (2012)Google Scholar
  30. 30.
    Skorin-Kapov, L., Ivesic, K., Aristomenopoulos, G., Papavassiliou, S.: Approaches for utility-based qoe-driven optimization of network resource allocation for multimedia services. In: Data Traffic Monitoring and Analysis. Lecture Notes in Computer Science, vol. 7754, pp. 337–358. Springer, Berlin (2013)Google Scholar
  31. 31.
    Soundararajan, R., Bovik, A.: Video quality assessment by reduced reference spatio-temporal entropic differencing. IEEE Trans. Circuits Syst. Video Technol. 23, 684–694 (2013)Google Scholar
  32. 32.
    Stockhammer, T.: Dynamic adaptive streaming over http: Standards and design principles. In: Proceedings of the Second Annual ACM Conference on Multimedia Systems, pp. 133–144. ACM (2011)Google Scholar
  33. 33.
    Team, R.P.: RYU SDN Framework. Release 1.0. RYU project team (2014).
  34. 34.
    Wang, N., Zhang, Y., Serrat, J., Gorricho, J.L., Guo, T., Hu, Z., Zhang, P.: A two-dimensional architecture for end-to-end resource management in virtual network environments. IEEE Netw. 26(5), 8–14 (2012)CrossRefGoogle Scholar
  35. 35.
    Zhang, M., Wu, C., Qiang, Y., Jiang, M.: Robust dynamic bandwidth allocation method for virtual networks. In: Proceedings of IEEE International Conference on Communications (ICC) (2012)Google Scholar
  36. 36.
    Zhou, L., Yang, Z., Rodrigues, J., Guizani, M.: Exploring blind online scheduling for mobile cloud multimedia services. IEEE Wirel. Commun. 20(3), 54–61 (2013)CrossRefGoogle Scholar
  37. 37.
    Zhou, L., Yang, Z., Wen, Y., Wang, H., Guizani, M.: Resource allocation with incomplete information for qoe-driven multimedia communications. IEEE Trans. Wirel. Commun. 12(8), 3733–3745 (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • R. L. Gomes
    • 1
    Email author
  • L. Bittencourt
    • 1
  • E. Madeira
    • 1
  • E. Cerqueira
    • 2
  • M. Gerla
    • 3
  1. 1.University of Campinas (UNICAMP)CampinasBrazil
  2. 2.Federal University of Para (UFPA)BelémBrazil
  3. 3.University of California, Los Angeles (UCLA)Los AngelesUSA

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