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

Software-Defined Networking (SDN) is an emerging network architecture characterized by the decoupling of the data plane from the control plane, as well as managing the whole network in a centralized logic. As the scale of networks continues to expand, the single-controller architecture is no longer able to meet the performance and reliability requirements of the network. Consequently, a logically centralized while physically distributed multi-controller architecture has been proposed, in which the number and locations of controllers must be determined, formulating the Controller Placement Problem (CPP). In order to solve the CPP and optimize the propagation latency, we propose a Convolutional Node Embedding and K-means Algorithm (CNEKA), which integrates Graph Convolutional Networks (GCN) with the K-means algorithm. More specifically, we adopt the principles of GCN to facilitate mutual information propagation among adjacent nodes, and then use the K-means algorithm to achieve graph segmentation through the low-dimensional embedding vectors calculated by GCN. The study demonstrates that the CNEKA algorithm significantly enhances performance in optimizing average and worst-case latency between controllers and switches, as well as overall network latency, particularly under high controller counts. The algorithm achieves a mere 1.10% deviation from the global optimum in average controller-switch latency, underscoring its high efficacy.