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A multipath resource updating approach for distributed controllers in software-defined network

软件定义网络中一种分布式控制器间的多路径资源更新方法

Abstract

Finding effective ways to collect the usage of network resources in all kinds of applications to ensure a distributed control plane has become a key requirement to improve the controller’s decision making performance. This paper explores an efficient way in combining dynamic NetView sharing of distributed controllers with the behavior of intra-service resource announcements and processing requirements that occur in distributed controllers, and proposes a rapid multipathing distribution mechanism. Firstly, we establish a resource collecting model and prove that the prisoner’s dilemma problem exists in the distributed resource collecting process in the Software-defined Network (SDN). Secondly, we present a bypass path selection algorithm and a diffluence algorithm based on Q-learning to settle the above dilemma. At last, simulation results are given to prove that the proposed approach is competent to improve the resource collecting efficiency by the mechanism of self-adaptive path transmission ratio of our approach, which can ensure high utilization of the total network we set up.

摘要

创新点

在分布式 SDN 环境下, 针对各类应用有效地收集网络资源已成为改善控制器决策能力及其性能的关键因素。 论文通过研究分布式控制器收集到的资源请求如何结合快速路径分发机制在域间资源的通告, 更新生成共享资源的动态网络视图, 并能为控制器决策提供有益帮助的方法。 首先, 建立资源收集模型并证明收集过程中存在着囚徒困境问题。 然后, 提出了基于 Q 学习的旁路路径选择算法和分流资源传输算法。 最后, 仿真结果表明自适应路径传输机制能有效地改善分布式控制器的资源收集及决策效率, 并提高全网的资源利用率。

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References

  1. 1

    ONF White Paper. Software-defined networking: the new norm for networks. Open Networking Foundation, 2012

  2. 2

    Koponen T, Casado M, Gude N, et al. Onix: a distributed control platform for large-scale production networks. In: Proceedings of the 9th USENIX Conference on Operating Systems Design and Implementation. USENIX Association Berkeley, 2010. 351–364

  3. 3

    Yaganeh S H, Tootoonchian A, Ganjali Y. On the scalability of software-defined networking. IEEE Commun Mag, 2013, 51: 136–141

  4. 4

    Tootoonchian A, Gorbunov S, Ganjali Y, et al. On controller performance in software-defined networks. In: Proceedings of the 2nd USENIX Conference on Hot Topics inManagement of Internet, Cloud, and Enterprise Networks and Services. USENIX Association Berkeley, 2012. 10

  5. 5

    Zuo Q Y, Chen M, Ding K, et al. On generality of the data plane and scalability of the control plane in software-defined networking. China Commun, 2014, 11: 55–64

  6. 6

    Hu J, Lin C, Li X Y, et al. Scalability of control planes for Software defined networks: modeling and evaluation. In: Proceedings of IEEE 22nd International Symposium on Quality of Service (IWQoS), Hong Kong, 2014. 147–152

  7. 7

    Lu H, Arora N, Zhang H, et al. HybNET: network manager for a hybrid network infrastructure. In: Proceedings of the Industrial Track of the 13th ACM/IFIP/USENIX International Middleware Conference. New York: ACM, 2013. 6

  8. 8

    Drutskoy D, Keller E, Rexford J. Scalable network virtualization in software-defined networks. IEEE Internet Comput, 2013, 11: 20–27

  9. 9

    Kreutz D, Ramos F, Verissimo P. Towards secure and dependable software-defined networks. In: Proceedings of the 2nd ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking. New York: ACM, 2013. 55–60

  10. 10

    Curtis A R, Mogul J C, Tourrilhes J, et al. DevoFlow: scaling flow management for high-performance networks. ACM SIGCOMM Comput Communn Rev, 2011, 41: 254–265

  11. 11

    Benson T, Anand A, Akella A, et al. MicroTE: fine grained traffic engineering for data centers. In: Proceedings of the 7th COnference on Emerging Networking Experiments and Technologies. New York: ACM, 2011. 8

  12. 12

    Kim H, Feamster N. Improving network management with software defined networking. IEEE Commun Mag, 2013, 51: 114–119

  13. 13

    Yu C, Lumezanu C, Singh V, et al. FlowSense: monitoring network utilization with zero measurement cost. In: Proceedings of the 14th International Conference on Passive and Active Measurement. Berlin/Heidelberg: Springer-Verlag, 2013. 31–41

  14. 14

    Jose L, Yu M, Rexford J. Online measurement of large traffic aggregates on commodity switches. In: Proceedings of the 11th USENIX Conference on Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services. USENIX Association Berkeley, 2011. 13

  15. 15

    Yu M, Lavanya J, Miao R. Software defined traffic measurement with OpenSketch. In: Proceedings of the 10th USENIX Conference on Networked Systems Design and Implementation. USENIX Association Berkeley, 2013. 29–42

  16. 16

    Yuan L, Chuah C N, Mohapatra P. Towards programmable network measurement. Trans Network, 2011, 19: 115–128

  17. 17

    Heller B, Sherwood R, Mc Keown N. The controller placement problem. In: Proceedings of the 1st Workshop on Hot Topics in Software Defined Networks. New York: ACM, 2012. 7–12

  18. 18

    Marconett D, Yoo S J B. FlowBroker: a software-defined network controller architecture for multi-domain brokering and reputation. J Netw Syst Manag, 2015, 23: 328–359

  19. 19

    Hassas Yeganeh S, Ganjali Y. Kandoo: a framework for efficient and scalable offloading of control applications. In: Proceedings of the 1st Workshop on Hot Topics in Software Defined Networks. New York: ACM, 2012. 19–24

  20. 20

    Thomas R W, Friend D H, Da Silva L A, et al. Cognitive networks. In: Arslan H, ed. Cognitive Radio, Software Defined Radio, and Adaptive Wireless Systems. Netherlands: Springer, 2007. 17–41

  21. 21

    Femminella M, Francescangeli R, Reali G, et al. An enabling platform for autonomic management of the future Internet. IEEE Network, 2011, 25: 24–32

  22. 22

    Reitblatt M, Foster N, Rexford J, et al. Consistent updates for software-defined networks: change you can believe in! In: Proceedings of the 10th ACM Workshop on Hot Topics in Networks. New York: ACM, 2011. 7

  23. 23

    Chen F, Wu C M, Wang B, et al. Dynamic load distributed with hop-by-hop forwarding based on max-min one-way delay. Sci China Inf Sci, 2014, 5: 062310

  24. 24

    Wu X C, Wu C M, Wang B, et al. Network view and cognitive mechanism for virtual network resource management based intelligent. Chin J Electron, 2014, 23: 574–578

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Correspondence to Chunming Wu.

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Wu, X., Wu, C., Lin, C. et al. A multipath resource updating approach for distributed controllers in software-defined network. Sci. China Inf. Sci. 59, 92301 (2016). https://doi.org/10.1007/s11432-016-5574-0

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Keywords

  • multipath
  • Pareto optimal
  • Q-learning

关键词

  • 软件定义网络
  • 多路径
  • 帕累托最优
  • Q 学习
  • 网络视图