PopFlow: a novel flow management scheme for SDN switch of multiple flow tables based on flow popularity

Abstract

Pipeline processing is applied to multiple flow tables (MFT) in the switch of software-defined network (SDN) to increase the throughput of the flows. However, the processing time of each flow increases as the size or number of flow tables gets larger. In this paper we propose a novel approach called PopFlow where a table keeping popular flow entries is located up front in the pipeline, and an express path is provided for the flow matching the table. A Markov model is employed for the selection of popular entries considering the match latency and match frequency, and Queuing theory is used to model the flow processing time of the existing MFTbased schemes and the proposed scheme. Computer simulation reveals that the proposed scheme substantially reduces the flow processing time compared to the existing schemes, and the difference gets more significant as the flow arrival rate increases.

Keywords

edge computing / SDN / pipeline processing / PopFlow / match frequency and latency / markov model-based prediction

References

[1] Aggarwal C, Srivastava K. Securing IOT devices using SDN and edge computing. In: Proceedings of the 2nd International Conference on Next Generation Computing Technologies. 2016, 877–882

[2] Open Networking Foundation. Openflow switch specification. Version 1.3.1, 2012

[3] McKeown N, Anderson T, Balakrishnan H, Parulkar G, Peterson L, Rexford J, Shenker S, Turner J. OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Computer Communication Review, 2008, 38(2): 69–74

[4] Kreutz D, Ramos F, Verissimo P, Rothenberg C, Azodolmolky S, Uhlig S. Software-defined networking: a comprehensive survey. Proceedings of the IEEE, 2015, 103(1): 14–76

[5] Open Networking Foundation. The benefits of multiple flow tables and ttps. Version 1.0, 2015

[6] Zhang H, Yan J. Performance of SDN routing in comparison with legacy routing protocols. In: Proceedings of 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery. 2015, 491–494

[7] Kotronis V, Dimitropoulos X, Ager B. Outsourcing the routing control logic: better internet routing based on SDN principles. In: Proceedings of the 11th ACM Workshop on Hot Topics in Networks. 2012, 55–60

[8] Long H, Shen Y, Guo M, Tang F. LABERIO: dynamic load-balanced routing in OpenFlow-enabled networks. In: Proceedings of the 27th IEEE International Conference on Advanced Information Networking and Applications. 2013, 290–297

[9] Mao Q, Shen W. A load balancing method based on SDN. In: Proceedings of the 7th International Conference on Measuring Technology and Mechatronics Automation. 2015, 18–21

[10] Reitblatt M, Canini M, Guha A, Foster N. Fattire: declarative fault tolerance for software-defined networks. In: Proceedings of the 2nd ACM SIGCOMMWorkshop on Hot Topics in Software Defined Networking. 2013, 109–114

[11] Kim H, Schlansker M, Santos J, Tourrilhes J, Turner Y, Feamster N. Coronet: fault tolerance for software defined networks. In: Proceedings of the 20th IEEE International Conference on Network Protocols. 2012, 1–2

[12] Guerzoni R, Trivisonno R, Vaishnavi I, Despotovic Z, Hecker A, Beker S, Soldani D. A novel approach to virtual networks embedding for SDN management and orchestration. In: Proceedings of 2014 IEEE Network Operations and Management Symposium. 2014, 1–7

[13] Li D, Wang S, Zhu K, Xia S. A survey of network update in SDN. Frontiers of Computer Science, 2017, 11(1): 4–12

[14] Wu Z, Jiang Y, Yang S. An efficiency pipeline processing approach for OpenFlow switch. In: Proceedings of the 41st IEEE Conference on Local Computer Networks. 2016, 204–207

[15] Ozcevik Y, Erel M, Canberk B. Spatio-temporal multi-stage OpenFlow switch model for software defined cellular networks. In: Proceedings of the 82nd IEEE Vehicular Technology Conference, 2015, 1–5

[16] Kanizo Y, Hay D, Keslassy I. Palette: distributing tables in softwaredefined networks. In: Proceedings of IEEE INFOCOM. 2013, 545–549

[17] Kang N, Liu Z, Rexford J, Walker D. Optimizing the one big switch abstraction in software-defined networks. In: Proceedings of the 9th ACM Conference on Emerging Networking Experiments and Technologies. 2013, 13–24

[18] Yu M, Rexford J, Freedman M, Wang J. Scalable flow-based networking with DIFANE. In: Proceedings of ACM SIGCOMM Computer Communication Review. 2011, 351–362

[19] Wang Y, Tai D, Zhang T, Jin L, Dai H, Liu B, Wu X. Flowshadow: a fast path for uninterrupted packet processing in sdn switches. In: Proceedings of the 11th ACM/IEEE Symposium on Architectures for Networking and Communications Systems. 2015, 205–206

[20] Wang Y, Tai D, Zhang T, Liu B. FlowShadow: keeping update consistency in software-based OpenFlow switches. In: Proceedings of the 24th IEEE/ACM International Symposium on Quality of Service. 2016, 1–10

[21] Kannan K, Banerjee S. Flowmaster: early eviction of dead flow on SDN switches. In: Proceedings of International Conference on Distributed Computing and Networking. 2014, 484–498

[22] Adan I, Resing J. Queueing theory. See Wikipedia, 2002

RIGHTS & PERMISSIONS

2020 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature