Research On Some Key Issues For Network Tomography

With the rapid development of the internet, network services and architecture are also undergoing profound changes. To design, control and manage the network successfully, we have to understand and grasp its internal characteristics (including all kinds of network parameters and topology). How to accurate and timely estimate network performance characteristics in more and more complex internet environment have become one of the forefront scientific problems which are focused by the academic community and industry community all over the world; Meantime, this problem is not only the basic research content of network behavior, but also the foundation of network optimize design, anomaly detection and analysis, network monitoring and evaluation and so on. In past, the traditional internet measure method usually resolves such a problem through the cooperation of node, however, as internet toward the evolution of distributive, no corporative, heterogeneous management and edge control, the traditional internet measure method do not meet fully the requirements of various applications on network measurement, for certain specific circumstances, we still need to study the network measurement method without the cooperation of individual servers and routers.By applying tomography theory which is successfully applied in fields such as medicine and seismology to the problems of network measurement in communication network, network tomography can infer the internal performance without the cooperation of internal node and transform network measurement problem into getting the solution of inversion problem in large-scale networks, which has become one of the focused new technologies. In the thesis, we review systematically the past progresses and important achievements at different phases in network tomography research area. The classification of network tomography methodologies are summarized systematically. We also point out some key issues for large scale network tomography. This thesis study profoundly some practical issues of network tomography from nonlinear, stable, unique and real-time solution point of view and results in following innovative achievements: 1. Research on network tomography method for link delay estimationThis thesis propose a novel link delay tracking algorithm based on the recurrent multilayer perceptron (RMLP) network, this algorithm is capable of tracking time-varying average delay behavior in non-stationary network and estimating the probability density distribution of internal delay characteristics without any prior information. Compare with existing delay tomography, RMLP method don't depend on the queue delay prior model and improve the stability, real-time of solution, obviously.2. Research on network tomography method for link loss estimationThis thesis propose a method of estimating the packet loss rate on unicast network which is based on multi-packet stripe probe. The multi-packet stripe probe is firstly sent to all receivers to cover all paths in the same time. Then, multi-packet stripe probe to the adjacent nodes or similar nodes are free to form two-packet stripe, three-packet stripe or four-packet stripe and we calculate absolute packet loss rate and conditional packet loss rate of the path to construct an over-determined equations. In order to having better accuracy and reducing computational complexity, we choose singular value decomposition (SVD) to calculate the Moore-Penrose generalized inverse matrix of the large-scale over-determined system equations, and finally get the link loss rate estimation. Compare with existing three-packet stripe method, our method can construct over-determined equations with more equations under the same number of probe packet circumstances, and that obtain more stability, flexibility and accurate estimation.3. Research on large scale traffic matrix estimation methodThis thesis propose a novel method of large scale traffic matrix estimation based on recurrent multilayer perceptron (RMLP) network. First of all we introduce the black-box models to model tracking traffic matrix without any prior information, then we use RMLP to resolve the solution of black-box models, finally, we get the entire based-RMLP traffic matrix tracking algorithm. Compare with existing delay tomography, RMLP method don't depend on the traffic prior model and improve the accuracy of estimates, stability and real-time demand obviously. Meanwhile, by using parallel algorithms and network distributed algorithms, we will further improve the real-time of solution. 4. Research on network topology discoveryBy introducing recursive unsupervised learning of Gaussian Mixture Models (GMM) to topology discovery approach, this thesis proposed a Fast Hierarchical Topology Estimation (FHTE) approach. By improving similarity metrics clustering process in network topology estimation, our proposed method evidently reducing the computation time under the same estimation accuracy circumstances compare to the Hierarchical Topology Estimation (HTE) approach.5. Research on network traffic classificationThis thesis propose a GMM-based semi-supervised classification method. Compare with supervised classification method, our approach takes advantage of only a few labeled data (about 10%) to identify different internet applications. Since obtaining the labeled instances is often difficult, expensive, or time consuming, the approach is particularly well suite for practical application and is more real-time. Semi-supervised classification method obtain more accurate classification compare to unsupervised classification method since it use a small amount of labeled instances to help us identify which Gaussian belongs to some class. Meanwhile, by analysis of semi-supervised classification method factors and their impact on classification performance, an optimum configuration is achieved for the GMM-based semi-supervised classification system, which further improve the classification performance of our approach.