RCP: A Temporal Clustering Algorithm for Real-time Controller Placement in Mobile SDN Systems

• Published in: Proceedings of the American Control Conference pp. 2767 - 2772
• Main Authors: Soleymanifar, Reza, Beck, Carolyn
• Format: Conference Proceeding
• Language: English
• Published: American Automatic Control Council 08.06.2022
• Subjects: Clustering algorithms; Heuristic algorithms; Processor scheduling; Real-time systems; Sensitivity; Temperature; Temperature sensors
• ISSN: 2378-5861
• DOI: 10.23919/ACC53348.2022.9867433

Software Defined Networking (SDN) is a recent paradigm in telecommunication networks that disentangles data and control planes and brings more flexibility to the network. The Controller Placement (CP) problem in SDN, which typically has a specific optimality criteria, is one of the primary problems in the SDN systems. Dynamic Controller Placement (DCP) enables a placement that is adaptable to inherent variability in network components. DCP has gained much attention in recent years, yet most solutions proposed in the literature cannot be implemented in real-time, which is a critical concern especially in UAV/drone based SDN networks where mobility is high and real-time updates are necessary. As conventional methods fail to be relevant to such scenarios, we propose a real-time control placement (RCP) algorithm. Namely, we propose a temporal clustering algorithm that provides real-time solutions for DCP, based on a control theoretic framework that is exponentially stable and converges to optimal placement of controllers. RCP has linear {\mathcal{O}}(N) iteration complexity with respect to the underlying network size (N), and also leverages the maximum entropy principle from information theory. This approach results in high quality solutions that are practically immune from getting stuck in poor local optima, which is a serious drawback conventional methods. We compare our work with a frame-by-frame approach and show its superiority, both in terms of speed and incurred cost, via simulations. According to our simulations RCP can be up to 25 times faster than the conventional frame-by-frame methods.