Methodology, Measurement and Analysis of Flow Table Update Characteristics in Hardware OpenFlow Switches
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
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AbstractSoftware-Defined Networking (SDN) and OpenFlow are actively being standardized and deployed. These deployments rely on switches that come from various vendors and differ in terms of performance and available features. Understanding these differences and performance characteristics is essential for ensuring successful and safe deployments.We propose a systematic methodology for SDN switch performance analysis and devise a series of experiments based on this methodology. The methodology relies on sending a stream of rule updates, while relying on both observing the control plane view as reported by the switch and probing the data plane state to determine switch characteristics by comparing these views. We measure, report and explain the performance characteristics of flow table updates in six hardware OpenFlow switches. Our results describing rule update rates can help SDN designers make their controllers efficient. Further, we also highlight differences between the OpenFlow specification and its implementations, that if ignored, pose a serious threat to network security and correctness.
CitationKuźniar M, Perešíni P, Kostić D, Canini M (2018) Methodology, Measurement and Analysis of Flow Table Update Characteristics in Hardware OpenFlow Switches. Computer Networks. Available: http://dx.doi.org/10.1016/j.comnet.2018.02.014.
SponsorsWe would like to thank Dan Levin and Miguel Peón for helping us get remote access to some of the tested switches. We also thank the representatives of the Pica8 P-3290, NoviSwitch 1132 and Switch X vendors for their quick and extensive responses that helped us understand some observations we made. The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/ 2007-2013) / ERC grant agreement 259110. This research is (in part) supported by European Union’s Horizon 2020 research and innovation programme under the ENDEAVOUR project (grant agreement 644960). This work is in part financially supported by the Swedish Foundation for Strategic Research.