A Qualitative and Comparative Performance Assessment of Logically Centralized SDN Controllers via Mininet Emulator

An alternative networking approach called Software Defined Networking (SDN) enables dynamic, programmatically efficient network construction, hence enhancing network performance. It splits a traditional network into a centralized control plane and a configurable data plane. Because the core component overseeing every data plane action is the controller in the control plane, which may contain one or more controllers and is thought of as the brains of the SDN network, controller functionality and performance are crucial to achieve optimal performances. There is much controller research available in the existing literature. Nevertheless, no qualitative comparison study of OpenFlow-enabled distributed but logically centralized controllers exists. This paper includes a quantitative investigation of the performance of several distributed but logically centralized SDN controllers in custom network scenarios using Mininet, as well as a thorough qualitative comparison of them. More precisely, we give a qualitative evaluation of their attributes and classify and categorize 13 distributed but logically centralized SDN controllers according to their capabilities. Additionally, we offer a comprehensive SDN emulation tool, called Mininet-based SDN controller performance assessment, in this study. Using six performance metrics—bandwidth, round-trip time, delay, jitter, packet loss, and throughput—this work also assesses five distributed but logically centralized controllers within two custom network scenarios (uniform and non-uniform host distribution). Our analysis reveals that the Ryu controller outperforms the OpenDayLight controller in terms of latency, packet loss, and round-trip time, while the OpenDayLight controller performs well in terms of throughput, bandwidth, and jitter. Throughout the entire experiment, the HyperFlow and ONOS controllers performed worst in all performance metrics. Finally, we discuss detailed research findings on performance. These experimental results provide decision-making guidelines when selecting a controller.