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

doi:10.1007/s11704-019-8417-5

Front. Comput. Sci.

2020, Vol. 14

Issue (6) : 146505 https://doi.org/10.1007/s11704-019-8417-5

RESEARCH ARTICLE

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

Cheng WANG¹, Kyung Tae KIM², Hee Yong YOUN²(

)

¹. College of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea
². College of Software, Sungkyunkwan University, Suwon 16419, Korea

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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

Corresponding Author(s): Hee Yong YOUN

Just Accepted Date: 02 March 2020 Issue Date: 20 July 2020

Cite this article:

Cheng WANG,Kyung Tae KIM,Hee Yong YOUN. PopFlow: a novel flow management scheme for SDN switch of multiple flow tables based on flow popularity[J]. Front. Comput. Sci., 2020, 14(6): 146505.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-019-8417-5
https://academic.hep.com.cn/fcs/EN/Y2020/V14/I6/146505

1	C Aggarwal, K Srivastava. Securing IOT devices using SDN and edge computing. In: Proceedings of the 2nd International Conference on Next Generation Computing Technologies. 2016, 877–882 https://doi.org/10.1109/NGCT.2016.7877534
2	Open Networking Foundation. Openflow switch specification. Version 1.3.1, 2012
3	N McKeown, T Anderson, H Balakrishnan, G Parulkar, L Peterson, J Rexford, S Shenker, J Turner. OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Computer Communication Review, 2008, 38(2): 69–74 https://doi.org/10.1145/1355734.1355746
4	D Kreutz, F Ramos, P Verissimo, C Rothenberg, S Azodolmolky, S Uhlig. Software-defined networking: a comprehensive survey. Proceedings of the IEEE, 2015, 103(1): 14–76 https://doi.org/10.1109/JPROC.2014.2371999
5	Open Networking Foundation. The benefits of multiple flow tables and ttps. Version 1.0, 2015
6	H Zhang, J Yan. 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 https://doi.org/10.1109/CyberC.2015.30
7	V Kotronis, X Dimitropoulos, B Ager. 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 https://doi.org/10.1145/2390231.2390241
8	H Long, Y Shen, M Guo, F Tang. 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	Q Mao, W Shen. A load balancing method based on SDN. In: Proceedings of the 7th International Conference on Measuring Technology and Mechatronics Automation. 2015, 18–21
10	M Reitblatt, M Canini, A Guha, N Foster. 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 https://doi.org/10.1145/2491185.2491187
11	H Kim, M Schlansker, J Santos, J Tourrilhes, Y Turner, N Feamster. Coronet: fault tolerance for software defined networks. In: Proceedings of the 20th IEEE International Conference on Network Protocols. 2012, 1–2
12	R Guerzoni, R Trivisonno, I Vaishnavi, Z Despotovic, A Hecker, S Beker, D Soldani. 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 https://doi.org/10.1109/NOMS.2014.6838244
13	D Li, S Wang, K Zhu, S Xia. A survey of network update in SDN. Frontiers of Computer Science, 2017, 11(1): 4–12 https://doi.org/10.1007/s11704-016-6125-y
14	Z Wu, Y Jiang, S Yang. An efficiency pipeline processing approach for OpenFlow switch. In: Proceedings of the 41st IEEE Conference on Local Computer Networks. 2016, 204–207 https://doi.org/10.1109/LCN.2016.43
15	Y Ozcevik, M Erel, B Canberk. Spatio-temporal multi-stage OpenFlow switch model for software defined cellular networks. In: Proceedings of the 82nd IEEE Vehicular Technology Conference, 2015, 1–5 https://doi.org/10.1109/VTCFall.2015.7391150
16	Y Kanizo, D Hay, I Keslassy. Palette: distributing tables in softwaredefined networks. In: Proceedings of IEEE INFOCOM. 2013, 545–549 https://doi.org/10.1109/INFCOM.2013.6566832
17	N Kang, Z Liu, J Rexford, D Walker. 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 https://doi.org/10.1145/2535372.2535373
18	M Yu, J Rexford, M Freedman, J Wang. Scalable flow-based networking with DIFANE. In: Proceedings of ACM SIGCOMM Computer Communication Review. 2011, 351–362 https://doi.org/10.1145/1851275.1851224
19	Y Wang, D Tai, T Zhang, L Jin, H Dai, B Liu, X Wu. 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 https://doi.org/10.1109/ANCS.2015.7110142
20	Y Wang, D Tai, T Zhang, B Liu. 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 https://doi.org/10.1109/IWQoS.2016.7590393
21	K Kannan, S Banerjee. Flowmaster: early eviction of dead flow on SDN switches. In: Proceedings of International Conference on Distributed Computing and Networking. 2014, 484–498 https://doi.org/10.1007/978-3-642-45249-9_32
22	I Adan, J Resing. Queueing theory. See Wikipedia, 2002

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