Plausible Performance Prediction of Simulated SDN-enabled Networks via Machine Learning
University of Würzburg, (Juli 2021)Zusammenfassung
In recent years, Software-Defined Networking (SDN) emerged as an innovative network architecture and gained increasing popularity due to its centralized network management. However, this central point induces a potential bottleneck and possible single-point-of-failure to the network. Therefore, so-called distributed architectures come into play, which keep the logical view centralized, but physically distributed. The goal of this thesis is to evaluate two opposing approaches and predict their performance via Machine Learning (ML). The simulation will be conducted on basis of a preexisting framework based on OMNeT++, which already implements two distributed SDN-architectures, ready for simulation.