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Abstract
Software-defined networking (SDN) has proven its superiority in addressing ordinary network problems, such as scalability, agility and security. This advantage of SDN comes because of its separation of the control plane from the data plane. Although many studies have focused on SDN management, monitoring, control and improving quality of service, only a few are focused on presenting what is used to generate traffic and collect data. The literature also lacks comparisons amongst the tools and methods used in this context. Therefore, this study introduces the recent tools used to simulate, generate and obtain traffic statistics from an SDN environment. In addition, the methods used in SDN data gathering are compared to explore the capability of each one and hence, determine the suitable environment for each method. The SDN testbed is simulated using Mininet software with tree topology and OpenFlow switches. An RYU controller was connected to control forwarding. The famous tools iperf3, Ping and python scripts are used to generate network datasets from selected devices in the network. Wireshark, the RYU application and the ovs-ofctl command are used to monitor and gather the dataset. Results show success in generating several types of network metrics to be used in the future for training machine or deep learning algorithms. Therefore, when generating data for the purpose of congestion control, iperf3 is the best tool, whilst Ping is useful when generating data for the purpose of detecting distributed denial-of-service attacks. RYU applications are the most suitable monitoring tool for obtaining all network details, such as the topology, characteristics and statistics of the components. Many obstacles and mistakes are also explored and listed to be prevented when researchers try to generate such datasets in their next scientific efforts.
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[21] http://mininet.org/
[22] https://iperf.fr/iperf-doc.php
[23] https://ryu.readthedocs.io/en/latest/ofproto_v1_3_ref.html
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