In recent years, Software Defined Networking (SDN) has emerged as a pivotal element not only in data-centers and wide-area networks, but also in next generation networking architectures such as Vehicular ad hoc network and 5G. SDN is characterized by decoupled data and control planes, and logically centralized control plane. The centralized control plane in SDN offers several opportunities as well as challenges. A key design choice of the SDN control plane is placement of the controller(s), which impacts a wide range of network issues ranging from latency to resiliency, from energy efficiency to load balancing, and so on. In this paper, we present a comprehensive survey on the controller placement problem (CPP) in SDN. We introduce the CPP in SDN and highlight its significance. We present the classical CPP formulation along with its supporting system model. We also discuss a wide range of the CPP modeling choices and associated metrics. We classify the CPP literature based on the objectives and methodologies. Apart from the primary use-cases of the CPP in data-center networks and wide area networks, we also examine the recent application of the CPP in several new domains such as mobile/cellular networks, 5G, named data networks, wireless mesh networks and VANETs. We conclude our survey with discussion on open issues and future scope of this topic.

A Survey on Controller Placement in SDN

With the development of smart grid technologies, residential and commercial loads have large potentialities to participate in demand response (DR) programs. This makes the data dimension reduction techniques and classification processing critical for the success of DR development. A novel load profile clustering method is proposed for load data classification which is based on the information entropy, piecewise aggregate approximation, and spectral clustering (SC). The variable temporal resolution technique is presented to model typical daily load datasets, and then an improved SC based on multi-scale similarities of distance and shape characteristics is proposed for clustering to obtain reasonable load classification. A case study with one hundred of commercial heating, ventilation, and air conditioning data analysis illustrates the approach. The results prove that the proposed method is feasible in terms of data dimension reduction, reasonable profile selection and classification, and the operation stability.

Clustering Load Profiles for Demand Response Applications

As a novel architecture, software-defined networking (SDN) is viewed as the key technology of future networking. The core idea of SDN is to decouple the control plane and the data plane, enabling centralized, flexible, and programmable network control. Although local area networks like data center networks have benefited from SDN, it is still a problem to deploy SDN in wide area networks (WANs) or large-scale networks. Existing works show that multiple controllers are required in WANs with each covering one small SDN domain. However, the problems of SDN domain partition and controller placement should be further addressed. Therefore, we propose the spectral clustering based partition and placement algorithms, by which we can partition a large network into several small SDN domains efficiently and effectively. In our algorithms, the matrix perturbation theory and eigengap are used to discover the stability of SDN domains and decide the optimal number of SDN domains automatically. To evaluate our algorithms, we develop a new experimental framework with the Internet2 topology and other available WAN topologies. The results show the effectiveness of our algorithm for the SDN domain partition and controller placement problems.

/pdf/a-k-self-adaptive-sdn-controller-placement-for-wide-area-5e07kazdxp.pdf

A K self-adaptive SDN controller placement for wide area networks

The construction process for a similarity matrix has an important impact on the performance of spectral clustering algorithms. In this paper, we propose a random walk based approach to process the Gaussian kernel similarity matrix. In this method, the pair-wise similarity between two data points is not only related to the two points, but also related to their neighbors. As a result, the new similarity matrix is closer to the ideal matrix which can provide the best clustering result. We give a theoretical analysis of the similarity matrix and apply this similarity matrix to spectral clustering. We also propose a method to handle noisy items which may cause deterioration of clustering performance. Experimental results on real-world data sets show that the proposed spectral clustering algorithm significantly outperforms existing algorithms.

An improved spectral clustering algorithm based on random walk

A new image segmentation method is proposed in the framework of Normalized Cuts to solve the perceptual grouping problem by means of graph partitioning, and the multiscale graph decomposition to obtain image features. Texture features is modeled with orientation histograms defined on the different scale level. The global optimal segmentation can be efficiently computed via graph cuts. The segmentation is implemented by partitioning a graph representing an image at the finest scale level to obtain accurate segmentation, while the weights of the graph are calculated from all the scales. Due to the reduced dimensionality based on texton, the speed of Normalized Cuts is increased. Efficiency and accuracy of the method is demonstrated on the nature images and remote sensing images segmentation.

A Normalized Cuts Based Image Segmentation Method

This paper exposes some intrinsic characteristics of the spectral clustering method by using the tools from the matrix perturbation theory. We construct a weight matrix of a graph and study its eigenvalues and eigenvectors. It shows that the number of clusters is equal to the number of eigenvalues that are larger than 1, and the number of points in each of the clusters can be approximated by the associated eigenvalue. It also shows that the eigenvector of the weight matrix can be used directly to perform clustering; that is, the directional angle between the two-row vectors of the matrix derived from the eigenvectors is a suitable distance measure for clustering. As a result, an unsupervised spectral clustering algorithm based on weight matrix (USCAWM) is developed. The experimental results on a number of artificial and real-world data sets show the correctness of the theoretical analysis.

/pdf/spectral-clustering-based-on-matrix-perturbation-theory-3350v0rdqh.pdf

Spectral clustering based on matrix perturbation theory

This paper proposes a sampling based hierarchical approach for solving the computational demands of the spectral clustering methods when applied to the problem of image segmentation. The authors first define the distance between a pixel and a cluster, and then derive a new theorem to estimate the number of samples needed for clustering. Finally, by introducing a scale parameter into the similarity function, a novel spectral clustering based image segmentation method has been developed. An important characteristic of the approach is that in the course of image segmentation one needs not only to tune the scale parameter to merge the small size clusters or split the large size clusters but also take samples from the data set at the different scales. The multiscale and stochastic nature makes it feasible to apply the method to very large grouping problem. In addition, it also makes the segmentation compute in time that is linear in the size of the image. The experimental results on various synthetic and real world images show the effectiveness of the approach.

Multiscale stochastic hierarchical image segmentation by spectral clustering

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Spectral clustering based on matrix perturbation theory

Multiscale stochastic hierarchical image segmentation by spectral clustering