OpenFlow data planes performance evaluation

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Abstract

Software-Defined Networks, in its essence, are the separation of the data and control planes of switching devices. The OpenFlow (OF) protocol is the most popular SDN protocol today, being available in many switches. This is due to the low implementation cost as well as the potential for innovative solutions in the network. Although OF is being used in many research papers and production networks, as far as we know, no work on the literature performs an extensive evaluation of the OpenFlow data planes. This evaluation helps network administrators choose which switch to use in their networks. Meanwhile, researchers are made aware of the limitations of existing OF switches. This article evaluates the performance and maturity of OF 1.0 on eleven hardware and software switches using POX, and also of the newer OF 1.3 on five switches using the ONOS controller. Our findings indicate that the performance variations among OF switches are significant. Packet delays vary by one order of magnitude in the evaluated equipment. Meanwhile, there is no performance impact when changing the packet size. Hence, the results highlight that researchers must be aware of issues such as variable jitter, the number of matches in tables as well as missing OF match fields.

Introduction

Software-Defined Networking (SDN) is a technology that is reshaping the way networks are operated and developed [1]. SDN simplified the creation of new networking solutions, mainly because of the separation of the data and control planes. The key benefits of this are the fast deployment of new services, which drive down the costs of network operators. Because of that, SDN is becoming a key architecture component on 5G systems [2]. SDN can even be used to support matching rules with domain names [3]. Furthermore, researchers are now able to perform tests in real environments, without affecting the traffic or the availability of production networks [1]. In this context, there is no doubt that the SDN paradigm attracted attention from both academia and industry.

There are several sophisticated SDN implementations, such as P4 (Programming Protocol-independent Packet Processors) and POF (Protocol-oblivious Forwarding) [4], [5]. However, OpenFlow is the most popular standard in production networks and research. This occurs because vendors can implement OpenFlow without significant difficulties.

Despite being widely employed in industry and academia, as far as we know, no work has evaluated the existing OpenFlow data planes. Such a study is important for operators because it provides an assessment that may guide the deployment of OpenFlow on their network. For researchers, this is useful to grasp the limitations of OpenFlow in current hardware. With this information in mind, better emulators and simulators can be built, by representing the performance and features of those switches more realistically. Many papers evaluate the performance of the OpenFlow Controllers [6], [7], [8], [9], however, most of them do not measure how the implementation of the data plane affects the network performance. Only [7], [8], [9] evaluate the data plane, however, their work does not measure the data plane separately from the control plane. It is important to properly investigate the data plane because the performance of the controller only affects the first packet in a flow. The data plane, however, will define the performance for the entire flow.

Given this context, this article evaluates the performance and maturity of the main features of OpenFlow 1.0 and 1.3 on both hardware and software switches. We consider a wide number of switches, varying from various off-the-shelf equipment to open source implementations of software switches running on PCs or embedded platforms (e.g. home routers). In a controlled environment, we systematically evaluate the implementation of the OpenFlow data planes by: (i) measuring the performance of the switch in terms of latency and jitter, (ii) assessing how the OpenFlow mode compares to the legacy L2 switching mode, and (iii) evaluating how OpenFlow operations (such as rule matches, packet rewrites, flowstats, and packetstats reports) perform. This article extends our previous work [10]1 by:

  • Adding OpenFlow 1.3 performance evaluation experiments;

  • Adding one more commercial switch to the comparison of OpenFlow 1.0 switches (Zodiac FX);

  • Testing a highly optimized version of Open vSwitch using the Intel DPDK suite of Linux kernel modifications, to understand how OS optimizations may improve the performance of software switches;

  • Adding high-performance switch evaluation;

Our results show that OpenFlow’s performance varies significantly. For example, packet delays vary by one order of magnitude among the evaluated switches, while the performance is not affected by the packet size. Moreover, we also note that some hardware switches perform as well as software switches, which may indicate a software implementation within the hardware. In sum, our article presents a glimpse into how OpenFlow is currently implemented on real devices. This is important for network administrators and academics to understand how OF switches work, allowing them to pick the best switch for their network.

The remainder of this article is structured as follows. Section 2 presents related work. Section 3 describes the evaluation methodology. Sections 4 Results for OpenFlow 1.0, 5 Results for OpenFlow 1.3 discuss the results for OpenFlow 1.0 and OpenFlow 1.3, respectively. Section 6 concludes the article and presents future work.

Section snippets

Related work

OpenFlow [11] is an open protocol in which a central element can inspect and modify the flow tables of the switching elements. This is possible via a standard API, where a computer (the control plane) sends queries and configuration messages to the network equipment (data plane) [1], [12]. OpenFlow is the most popular SDN platform in academia and industry [13]. Companies such as HP, NEC, Pronto, Extreme, Cisco, Brocade, Juniper, and Huawei already sell OF-capable devices [1]. Thus, it is

Scientific methodology and evaluation scenario

This section is divided into two, describing the evaluation methodology for OpenFlows 1.0, and 1.3, respectively. The methodology is different due to the following reason. The controller used for the evaluation of OF 1.0 does not support OF 1.3. Hence, we would have to rerun the tests for OF 1.0 performed in [10] using a new controller. However, some of the tested switches were not available anymore, because they are currently in use in production networks. To grow the list of switches, instead

Results for OpenFlow 1.0

This section presents the results of the performance evaluation of different hardware and software OF 1.0 compliant switches. Here, we extend the results of our previous work [10] to two additional switches, DPDK and Zodiac.

Results for OpenFlow 1.3

This section presents the results of the data plane performance evaluation of different hardware and software OpenFlow 1.3 compliant switches. The focus of this scenario is on the new functionality brought by OF 1.3, so we do not replicate the test cases performed in OF 1.0. Further, since DPDK performed better than Open vSwitch, this section considers only DPDK in the software switch category.

Conclusion

In this article, we evaluate software and hardware OpenFlow switches implementing OF versions 1.0 and 1.3. The objective is to understand how mature the production, as well as research-oriented switches, are because the use of OpenFlow is growing in academia and commercial deployments. Furthermore, the amount of OF-compliant devices increases daily.

Our evaluation considered eight commercial hardware switches, one academic hardware platform (NetFPGA), and two open-source software switches. The

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors would like to thank CAPES, CNPq, FAPEMIG for their financial support in this research. This research has been partly funded by the FUTEBOL project (European Project H2020 Grant No. 688941, jointly funded by RNP). They also thank Christian E. Rothenberg for his invaluable reviews and RNP for providing the Datacom router for evaluation.

LEONARDO C. COSTA received his B.Sc (2013). and M.Sc (2016). degrees in Computer Science from the Federal University of Juiz de Fora, Brazil. From 2016 to 2018, he was a research assistant at the National Laboratory for Scientific Computing, Brazil. Currently, he is an IT staff member of the Court of Justice in São Paulo, Brazil. His areas of interest are network science, data science, and SDN.

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    LEONARDO C. COSTA received his B.Sc (2013). and M.Sc (2016). degrees in Computer Science from the Federal University of Juiz de Fora, Brazil. From 2016 to 2018, he was a research assistant at the National Laboratory for Scientific Computing, Brazil. Currently, he is an IT staff member of the Court of Justice in São Paulo, Brazil. His areas of interest are network science, data science, and SDN.

    Alex Borges Vieira is an associate professor at the Computer Science department of Universidade Federal de Juiz de Fora. He has productivity in research scholarship (Bolsa PQ-CNPq) level 2. Currently, he is a visiting professor at the National Laboratory for Scientific Computing (LNCC), Brazil. His research interests include sensor networks; IoT, network characterization, modeling, and analysis; and network science. Vieira received a Ph.D. in Computer Science from UFMG. He is a member of IEEE, ACM and SBC.

    Erik de Britto e Silva is currently in his Ph.D. work in Applied Engineering at University of Antwerp & imec & Internet and Data Lab (IDLab). He holds an M.Sc. in Computer Science from Universidade Federal de Minas Gerais (2016) and a B.Sc. in Electrical Engineering from Universidade Federal de Minas Gerais (1985). His areas of interest are computer networks, software-defined networks, wireless networks and 5G vehicular communications: CV2X and Cloud/Edge Sliced Networks. Open Researcher & Contributor ID (ORCID): https://orcid.org/0000-0002-0468-8514.

    Daniel F. Macedo is an Associate Professor at the Computing Department (DCC) in Universidade Federal de Minas Gerais (UFMG), Brazil. He has productivity in research scholarship (Bolsa PQ-CNPq) level 2. He was a post-doc researcher in UFMG, Brazil. He holds a Ph.D. in Computer Science from Université Pierre et Marie Curie-ParisVI (2009). He also holds a M.Sc. and a B.Sc. in Computer Science from Universidade Federal de Minas Gerais (2006). His research interests are autonomic computing, wireless networks and network management.

    Luiz F. M. Vieira is an Associate Professor at the Computer Science Department (DCC) in Universidade Federal de Minas Gerais (UFMG), Brazil. He has productivity in research scholarship (Bolsa PQ-CNPq) level 1D. He holds a Ph.D. in Computer Science from University of California Los Angeles (UCLA) (2009). He also holds an M.Sc. and a B.Sc. in Computer Science from Universidade Federal de Minas Gerais. His research interests are computer networks, wireless networks and internet of things.

    Marcos A. M. Vieira is an Associate Professor at the Computer Science Department (DCC) in Universidade Federal de Minas Gerais (UFMG), Brazil. He has productivity in research scholarship (Bolsa PQ-CNPq) level 2. He holds a Ph.D. in Computer Science from University of Southern California (USC) (2010). Healso holds a M.Sc and a B.Sc. in Computer Science from Universidade Federal de Minas Gerais. He did a postdoctoral year at the University of Southern California (2018–19). His research interests are computer networking, wireless networks and software-defined networking.

    Manoel R.M. Júnior is currently enrolled in a bachelor’s degree in Computer Science at the Federal University of Minas Gerais (UFMG), and holds a Computer Networks technician title by CEFET-MG. Has experience in software development and is interested in networks and distributed systems, data science, and development technologies in general.

    André Vinícius Gomes Santos Gonçalves holds a B.Sc. in Telecommunications Engineering from Universidade Federal de São João del-Rei (UFSJ) and a M.Sc. degree in Computer Science from Universidade Federal de Minas Gerais (UFMG). He is currently working toward his Ph.D. degree in Electronic and Electrical Engineering at the Trinity College Dublin (TCD). His research interests are mainly related to applications of machine learning, cognitive and self-organizing networks. Open Researcher & Contributor ID (ORCID): https://orcid.org/0000-0003-3612-6847

    Geraldo C.C. Gomes is a founding partner of Mérito Soluçø=oes in Lavras, Minas Gerais. He holds a B.S. in Information Systems from UFLA. His academic work is in the area of software-defined networks (SDN). Currently, he works with implementation and consulting in the area of networks and computer systems in healthcare.

    Luiz Henrique Andrade Correia is Associate Professor of the Department of Computer Science (DCC) of the Universidade Federal de Lavras (UFLA), Brazil. He holds a Ph.D. in Computer Science from the Universidade Federal de Minas Gerais (2006), a Master’s degree in Electrical Engineering from the Universidade Federal de Itajubá (1995) and a degree in Electrical Engineering from the Federal University of Juiz de Fora (1990). He did a postdoctoral year at the Universidade de Coimbra, Portugal (2012). His areas of interest are computer networks, cognitive radio networks, mobile networks, opportunistic networks, WSN applications, vehicular networks, security service and quality (QoS). He is a member of IEEE and SBC.

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