CNEKA : An Algorithm for SDN Controller Placement Based on Graph Convolutional Networks

With the rapid development of software‐defined networking (SDN), the single‐controller architecture is unable to meet the performance and reliability requirements of the whole system. Consequently, a distributed multicontroller architecture has been proposed, in which the number and locations of controllers must be determined rigorously, formulating the controller placement problem (CPP). In order to solve the CPP by optimizing the propagation latency, we propose a convolutional node embedding and K‐means algorithm (CNEKA), which integrates information propagation among adjacent nodes and calculation of embedding vectors by graph convolutional networks (GCNs) with the graph segmentation by K‐means algorithm. As far as we know, we are the first paper to apply GCN to solving CPP. The study demonstrates that the CNEKA algorithm significantly enhances performance in optimizing average and worst‐case latency between controllers and switches, as well as the propagation latency of the whole network, in varied experimental conditions. CNEKA achieves up to a 64.18% reduction in propagation latency when compared to other algorithms, and maintains a high stability with a fluctuation less than 7.6% when repeating the same experiments for several times. Moreover, CNEKA can always find the optimal or near‐optimal solution with an error less than 11.15% when compared with the global optimal solution.