SDN CLUSTERING DATASET (WITH NO WIRED CONNECTION)

Future 6G networks will consist of fully soft-warized networks that incorporate in-network intelligence for self-management. However, this intelligent management will require massive data mining, analytics, and processing. Therefore, we need resources like quantum technologies to help achieve 6G key performance indicators. We use Quantum Machine Learning (QML) to solve the controller placement problem for a multi-controller Software Defined Network (SDN). Network delay depends on the controller’s position. Thus, it is critical to choose controllers at locations that minimize latency between the controllers and their associated switches. By using different types of datasets (uniformly distributed and Gaussian distributed datasets), the experimental results indicate that QML can accelerate the computational query of SDN clustering as compared to classical machine learning (like K-means) with comparable latency. To the best of our knowledge, this is the first work that applies QML to solve SDN’s controller placement problem.