QoS for SDN-Based Fat-Tree Networks

  • Haitham GhalwashEmail author
  • Chun-Hsi Huang
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 70)


Software-defined Networks (SDNs) are the new network paradigm providing, programmability, agility, and centralized management. In this paper, we show how to leverage the SDN centralized controller to improve the network utilization and the traffic performance. On top of the SDN controller, new modules are added to help finding single and multi-path routes between communicating devices. Flow rules are automatically installed into the designated switches to provide the required paths. The behavior and performance of different types of traffic, namely, UDP, TCP, VOIP, and a Big-data application traffic are investigated. The traffic forwarding is based on either the controller built in layer 2 switching “odl-l2switch” feature or single/multi-path selection based on the supplemented modules. Experimental results based on metrics such as delay, jitter and packet drops are presented for each forwarding option. The results disclosed the advantage of having the developed modules on top of the controller for all traffic types. The OpenDaylight controller for OpenFlow switches, in a fat-tree network, is used for experiments. For a fair comparison of different traffic types, a monitoring module is built on top of the controller for collecting ports statistics, analyzing and monitoring.


QoS SDN Fat-Tree Docker Hadoop 



This work was supported by the U.S. Department of Education’s GAANN Fellowship through the Department of Computer Science and Engineering at the University of Connecticut.


  1. 1.
    Cisco Systems: Cisco Visual Networking Index: Forecast and Methodology, 2015–2020. White Paper (2016)Google Scholar
  2. 2.
    Andrus, B., Vegas Olmos, J.J., Mehmeri, V., Monroy, I.T., Spolitis, S., Bobrovs, V.: SDN data center performance evaluation of torus and hypercube interconnecting schemes. In: Proceedings—2015 Advances in Wireless and Optical Communications, Riga, Latvia, pp. 110–112 (2015)Google Scholar
  3. 3.
    Ghalwash, H., Huang, C.: Software-defined extreme scale networks for bigdata applications. In: High Performance Extreme Computing Conference, Waltham, MA, USA (2017)Google Scholar
  4. 4.
    Fundation ONF: Software-Defined Networking : The New Norm for Networks. ONF White Paper (2012)Google Scholar
  5. 5.
    McKeown, N., Anderson, T., Balakrishnan, H., Parulkar, G., Peterson, L., Rexford, J., Shenker, S., Turner, J.: OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Comput. Commun. Rev. 38, 69–74 (2008)CrossRefGoogle Scholar
  6. 6.
    Karakus, M., Durresi, A.: Quality of service (QoS) in software defined networking (SDN): a survey. J. Netw. Comput. Appl. 80, 200–218 (2017)CrossRefGoogle Scholar
  7. 7.
    Li, F., Cao, J., Wang, X., Sun, Y.: A SDN-based QoS guaranteed technique for cloud applications. IEEE Access 5, 229–241 (2017)Google Scholar
  8. 8.
    Xu, C., Chen, B., Qian, H.: Quality of service guaranteed resource management dynamically in software defined network. J. Commun. 10, 843–850 (2015)Google Scholar
  9. 9.
    Yan, J., Zhang, H., Shuai, Q., Liu, B., Guo, X.: HiQoS: an SDN-based multipath QoS solution. China Commun. 12, 123–133 (2015)CrossRefGoogle Scholar
  10. 10.
    Trajano, A.F.R., Fernandez, M.P.: uLoBal : Enabling In-Network Load Balancing for Arbitrary Internet Services on SDN, pp 62–67 (2016)Google Scholar
  11. 11.
    Desai, A.: Advanced Control Distributed Processing Architecture (ACDPA) Using SDN and Hadoop for Identifying the Flow Characteristics and Setting the Quality of Service (QoS) in the Network, pp. 784–788 (2015)Google Scholar
  12. 12.
    Narayan, S., Bailey, S., Daga, A.: Hadoop acceleration in an openflow-based cluster. In: Proceedings—2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC (2012)Google Scholar
  13. 13.
    Hong, W., Wang, K., Hsu, Y.H.: Application-aware resource allocation for SDN-based cloud datacenters. In: Proceedings—2013 International Conference on Cloud Computing and Big Data, pp. 106–110, Santa Clara, CA, USA (2013)Google Scholar
  14. 14.
    Hamad, D.J., Yalda, K.G., Okumus, I.T.: Getting traffic statistics from network devices in an SDN environment using OpenFlow. In: Information Technology and Systems 2015, Sochi, Russia, pp. 951–956 (2016)Google Scholar
  15. 15.
    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. 1–6, Monterey, CA, USA (2010)Google Scholar
  16. 16.
    Al-Fares, M., Loukissas, A., Vahdat, A.: A scalable, commodity data center network architecture. ACM SIGCOMM Comput. Commun. Rev. 38, 63–74 (2008)CrossRefGoogle Scholar
  17. 17.
    Saleh, A.: Evolution of the architecture and technology of data centers towards exascale and beyond. In: Optical Fiber Communication Conference/National Fiber Optic Engineers Conference, Anaheim, California, USA (2013)Google Scholar
  18. 18.
    Bradonjić, M., Saniee, I., Widjaja, I.: Scaling of capacity and reliability in data center networks. Perform Eval. Rev. 42, 3–5 (2014)CrossRefGoogle Scholar
  19. 19.
    Ghalwash, H., Huang, C.: On SDN-based extreme-scale networks. In: High Performance Extreme Computing Conference, Waltham, MA, USA (2016)Google Scholar
  20. 20.
    Botta, A., Dainotti, A., Pescap, A.: A tool for the generation of realistic network workload for emerging networking scenarios. Comput. Netw. 56, 3531–3547 (2012)CrossRefGoogle Scholar
  21. 21.
    Peuster, M., Karl, H., Van Rossem, S.: MeDICINE : rapid prototyping of production-ready network services in multi-PoP environments. In: 2016 IEEE Conference on Network Function Virtualization and Software Defined Networks, Palo Alto, California, USA (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of ConnecticutStorrsUSA

Personalised recommendations