GOKA: A Network Partition and Cluster Fusion Algorithm for Controller Placement Problem in SDN

Abstract

Software Defined Networking (SDN) is a new promising network architecture, with the property of decoupling the data plane from the control plane and centralizing the network topology logically, making the network more agile than traditional networks. However, with the continuous expansion of network scales, the single-controller SDN architecture is unable to meet the performance requirements of the network. As a result, the logically centralized and physically separated SDN multi-controller architecture comes into being, and thereupon the Controller Placement Problem (CPP) is proposed. In order to minimize the propagation latency in Wide Area Network (WAN), we propose Greedy Optimized K-means Algorithm (GOKA) which combines K-means with greedy algorithm. The main thought is to divide the network into multiple clusters, merge them greedily and iteratively until the given number of controllers is satisfied, and place a controller in each cluster through the K-means algorithm. With the purpose of proving the effectiveness of GOKA, we conduct experiments to compare with Pareto Simulated Annealing (PSA), Adaptive Bacterial Foraging Optimization (ABFO), K-means and K-means[Formula: see text] on 6 real topologies from the Internet Topology Zoo and Internet2 OS3E. The results demonstrate that GOKA has a better and more stable solution than other four heuristic algorithms, and can decrease the propagation latency by up to [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] in contrast to PSA, ABFO, K-means and K-means[Formula: see text], respectively. Moreover, the error rate between GOKA and the best solution is always less than [Formula: see text], which promises the precision of our proposed algorithm.