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The Controller Placement Problem (CPP) is a key research topic in Software Defined Network (SDN), as the communication delay is influenced by the position of controllers and switches. On that basis, the network failures may happen occasionally, which can cause the increase of propagation latency and the reduction of network performance. As a result, it is essential to research the Controller Placement problem for Link Failures (CPLF). In this paper, authors propose a method based on the cross entropy to solve CPP after link failures, and adopt the Halton sequence to reduce the computation overhead of simulating link failures while guaranteeing the accuracy. In the experiments, we measure and compare the worst-case delay among three methods: our proposed cross entropy-based controller placement algorithm, the optimized controller placement algorithm and a greedy-based controller placement algorithm, and conduct experiments on six real network topologies. The experimental results verify that our proposed method can reduce the worst-case delay by [Formula: see text] in comparison with GPA. Moreover, the proposed method can always find optimized controller placement schemes no matter how the network scale or the number of controller varies, with a less than [Formula: see text] error when compared with the optimal solution.

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.