ML-based Performance Prediction of SDN using Simulated Data from Real and Synthetic Networks

With increasing digitization and the emergence of the Internet of Things, more and more devices communicate with each other, resulting in a drastic growth of communication networks. Consequently, managing these networks, too, becomes harder and harder. Thus, Software-defined Networking (SDN) is employed, simplifying the management and configuration of networks by introducing a central controlling entity, which makes the network programmable via software and ultimately more flexible. As the SDN controller may impose scalability and elasticity issues, distributed controller architectures are utilized to combat this potential performance bottleneck. However, these distributed architectures introduce the need for constant synchronization to keep a centralized network view, and controller instances need to be placed in appropriate locations. As a result, thoroughly designing SDN-enabled networks with respect to a multitude of performance metrics, e. g., latency and induced traffic, is a challenging task. To assist in this process, we train a performance prediction model based on properties which are available during the network planning phase. We utilize a simulation-based approach for data collection to cover a large parameter space, simulating a variety of networks and controller placements for two opposing SDN architectures. On basis of this dataset, we apply Machine Learning (ML) to solve the performance prediction as a regression problem.

Automated summary

With the advancement of digitization and the Internet of Things, the increase in device communication has led to the need for effective management of communication networks. This has led to the adoption of Software-defined Networking (SDN), which simplifies network management and configuration through the introduction of a central controlling entity. However, the scalability and elasticity of SDN controllers pose challenges, leading to the use of distributed controller architectures. In order to overcome these challenges and design SDN-enabled networks effectively, a performance prediction model is trained using Machine Learning based on network properties available during the planning phase.