Evaluation and Performance Analysis of the Ryu Controller in Various Network Scenarios

TL;DR

This paper evaluates the performance of the Ryu SDN controller across various network scenarios using Mininet, providing insights into its efficiency and laying groundwork for future SDN controller assessments.

Contribution

It offers a comprehensive performance analysis of Ryu in different network topologies, introducing a Python-based testing framework for SDN controller evaluation.

Findings

Ryu performs reliably across multiple network scenarios

The Python script enables efficient automation of experiments

Performance metrics highlight strengths and limitations of Ryu

Abstract

Software-defined networking (SDN) represents a revolutionary shift in network technology by decoupling the data plane from the control plane.}In this architecture, all network decision-making processes are centralized in a controller, meaning each switch receives routing information from the controller and forwards network packets accordingly. This clearly highlights the crucial role that controllers play in the overall performance of SDN. Ryu is one of the most widely used SDN controllers, known for its ease of use in research due to its support for Python programming. This makes Ryu a suitable option for experimental and academic studies. In this research, we evaluate the performance of the Ryu controller based on various network metrics and across different network topologies. For experimental analysis, we use Mininet, a powerful network emulation tool that enables the creation of diverse network structures and the connection of switches to controllers. To facilitate the experiments, we developed a Python-based script that executes various network scenarios, connects to different controllers, and captures and stores the results. This study not only provides a comprehensive performance evaluation of the Ryu controller but also paves the way for evaluating other SDN controllers in future research.