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Mobile Networks and Applications

, Volume 19, Issue 4, pp 572–582 | Cite as

Quality of Service Modelling of Virtualized Wireless Networks: A Network Calculus Approach

  • Lianming Zhang
  • Jia Liu
  • Kun Yang
Article

Abstract

Wireless network virtualization is an emerging technology that logically divides a wireless network element, such as a base station (BS), into multiple slices with each slice serving as a standalone virtual BS. In such a way, one physical mobile wireless network can be partitioned into multiple virtual networks each operating as an independent wireless network. Wireless virtual networks, as composed of these virtual BSs, need to provide quality of service (QoS) to mobile end user services. Key QoS parameters include buffer queue length, network delay and effective bandwidth, in particular their upper bound forms. This paper presents a QoS model for such a wireless virtual network addressing these parameters. This QoS model considers resources of both physical nodes and virtual nodes and provides a realistic modelling of the delay and bandwidth behaviours of wireless virtual networks. Network calculus (NC), which usually provides finer insight into a system, is utilized to fulfil the modelling task. The numerical results have shown the effectiveness of the proposed model. The model is useful for both off-line network planning and online network admission control.

Keywords

Wireless network virtualization QoS modelling Upper bound delay Upper bound bandwidth Network calculus 

References

  1. 1.
    Lu X, Yang K, Liu Y, Zhou D, Liu S (2013) An elastic resource allocation algorithm enabling wireless network virtualization. Wiley Int J Wirel Commun Mob ComputGoogle Scholar
  2. 2.
    Liu J, Zhang L, Yang K (2013) Modeling guaranteed delay of virtualized wireless networks using network calculus. In: the 10th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MOBIQUITOUS 2013), Tokyo, JapanGoogle Scholar
  3. 3.
    Matos R, Marques C, Sargento S, Hummel KA, Meyer H (2011) Analytical modeling of context-based multi-virtual wireless mesh networks. Elsevier Int J Ad hoc Netw 13:191–209CrossRefGoogle Scholar
  4. 4.
    Le Boudec J-Y, Thiran P (2004) Network calculus. Springer Verlag, BerlinGoogle Scholar
  5. 5.
    Fidler M (2010) A survey of deterministic and stochastic service curve models in the network calculus. IEEE Commun Surv Tutor 12(1):59–86CrossRefGoogle Scholar
  6. 6.
    Ciucu F, Schmitt J (2012) Perspectives on network calculus—no free lunch but still good value. ACM SIGCOMM Comput Commun Rev 42(4):311–322CrossRefGoogle Scholar
  7. 7.
    Zhang L, Yu J, Deng X (2011) Modelling the guaranteed QoS for wireless sensor networks: a network calculus approach. EURASIP J Wireless Comm Networking 82Google Scholar
  8. 8.
    Chowdhury NMMK, Boutaba R (2009) Network virtualization: state of the art and research challenges. IEEE Commun Mag 47(7):20–26CrossRefGoogle Scholar
  9. 9.
    Schaffrath G, Werle C, Papadimitriou P, Feldmann A, Bless R, Greenhalgh A, Wundsam A, Kind M, Maennel O, Mathy L (2009) Network virtualization architecture: proposal and initial prototype. In: The1st ACM workshop on Virtualized infrastructure systems and architectures (VISA ’09), Barcelona, Spain, pp 63–72Google Scholar
  10. 10.
    Chowdhury M, Rahman MR, Boutaba R (2012) ViNEYard: virtual network embedding algorithms with coordinated node and link mapping. IEEE/ACM Trans Networking 20(1):206–219CrossRefGoogle Scholar
  11. 11.
    Kokku R, Mahindra R, Zhang H, Rangarajan S (2012) NVS: a substrate for virtualizing wireless resources in cellular networks. IEEE/ACM Trans Networking 20(5):1333–1346CrossRefGoogle Scholar
  12. 12.
    Ahn S, Yoo C (2011) Network interface virtualization in wireless communication for multi-streaming service. In: International Symposium on Consumer Electronics (ISCE2011), Singapore, pp 67–70Google Scholar
  13. 13.
    Tao M, Liang YC, Zhang F (2008) Resource allocation for delay differentiated traffic in multiuser OFDM systems. IEEE Trans Wirel Commun 7(6):2190–2201CrossRefGoogle Scholar
  14. 14.
    Hui DSW, Lau VKN, Wong HL (2007) Cross-layer design for OFDMA wireless systems with heterogeneous delay requirements. IEEE Trans Wirel Commun 6(8):2872–2880CrossRefGoogle Scholar
  15. 15.
    Zarakovitis CC, Ni Q, Skordoulis DE, Hadjinicolaou MG (2012) Power-efficient cross-layer design for OFDMA systems with heterogeneous QoS, imperfect CSI, and outage considerations. IEEE Trans Veh Technol 61(2):781–798CrossRefGoogle Scholar
  16. 16.
    Schmitt JB, Zdarsky FA, Fidler M (2008) Delay bounds under arbitrary multiplexing: when network calculus leaves you in the lurch.... In: 27th IEEE International Conference on Computer Communications (INFOCOM’08), Phoenix, AZ, USAGoogle Scholar
  17. 17.
    Bouillard A, Jouhet L, Thierry E (2010) Tight performance bounds in the worst-case analysis of feed-forward networks. In: the 29th IEEE International Conference on Computer Communications (INFOCOM’10), San Diego, CA, USA, pp 1316–1324Google Scholar
  18. 18.
    Schmitt JB, Zdarsky FA, Thiele L (2007) A comprehensive worst-case calculus for wireless sensor networks with in-network processing. In: The 28th IEEE International Real-Time Systems Symposium (RTSS’07), Tucson, AZ, USA, pp 193–202Google Scholar
  19. 19.
    Zhang L (2008) Bounds on end-to-end delay jitter with self-similar input traffic in ad hoc wireless network. In: 2008 ISECS International Colloquium on Computing, Communication, Control, and Management (CCCM’08), Guangzhou, China, pp 538–541Google Scholar
  20. 20.
    Al-Zubaidy H, Liebeherr J, Burchard A (2013) A (min, ×) network calculus for multi-hop fading channels. In: International Conference on Computer Communications (INFOCOM2013), Turin, Italy, pp 1833–1841Google Scholar
  21. 21.
    CiucuF, Schmitt J (2014) On the catalyzing effect of randomness on the per-flow throughput in wireless networks. In: International Conference on Computer Communications (INFOCOM2014), Toronto, Canada.Google Scholar
  22. 22.
    Duan Q (2012) Analysis on quality of service provisioning for communication services in network virtualization. J Commun 7(2):143–154CrossRefGoogle Scholar
  23. 23.
    Huang J, Xu C, Duan Q, Ma Y, Muntean G-M (2012) Novel end-to-end quality of service provisioning algorithms for multimedia services in virtualization-based future Internet. IEEE Trans Broadcast 58(4):569–579CrossRefGoogle Scholar
  24. 24.
    Drutskoy D, Keller E, Rexford J (2013) Scalable network virtualization in software-defined networks. IEEE Internet Comput 17(2):20–27CrossRefGoogle Scholar
  25. 25.
    Azodolmolky S, Nejabati R, Pazouki M, Wieder P, Yahyapour R, Simeonidou D (2013) An analytical model for Software Defined Networking: a network calculus-based approach. In: Global Communications Conference (GLOBECOM2013), Atlanta, GA, USAGoogle Scholar
  26. 26.
    Fidler M, Sander V (2004) A parameter based admission control for differentiated services networks. Comput Netw 44(4):463–47CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.College of Physics and Information ScienceHunan Normal UniversityChangshaChina
  2. 2.School of Computer Science & Electronic Engineering (CSEE)University of EssexColchesterUK

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