Research On Measurement Of Virtual Test Network Based On Tomography

Virtual test technology has been widely applied in the process of weapondevelopment because of the advantages of low cost and low risk, and has becomethe trend of military test. In the system constructed by virtual test technology, thetime-space consistency is the key issue of the correctness and fairness of the system,and it also assures that the test results are real and effective. In order to ensure theauthenticity of the virtual test process, it is necessary to obtain the networkperformance parameters and topology information in real time, and adjust thedeployment of the entities in each network node. Therefore, the measurement ofvirtual test network performance parameters and topologies are the basis ofimproving the performance of the virtual test system, and the real-time measurementmethods are the prerequisites of authenticity and validity for the virtual test.With the development of virtual test technology, virtual test network tends to beheterogeneous and non-cooperative because of the test mode of wide areas, acrosssystem boundaries and virtual-actual combination. It is difficult to meet the needs ofthe virtual test network measurement by traditional direct measurement mode.Recently, network tomography is proposed to measure the internal networkparameters without the cooperaton of the internal nodes, which has become one ofthe focused research technologies in the field. Currently, the research of networktomography is focused on the measurement accuracy and implemented on Internet.It is difficult to meet the real-time requirements of the vitual test networkmeasurement using existing methods. In order to improve the measurementefficiency and accuracy, and reduce the network load caused by the measurementprocess, some methods are proposed in the fields of network parameters (loss rateand delay) measurement and network topology inference with network tomography.The main contents are as follows:In the field of network tomography, the number of the measurement paths is atleast equal to the rank of the routing matrix, which determines the efficiency of themeasurement and the network load caused by the measurement process. In order toimprove the accuracy and efficiency of the inference method for the link loss rate, anovel link loss rate inference method is proposed based on minimal cover set. Theproposed method decreases the number of the end-to-end paths by reducing the rankof the routing matrix caused by the minimal cover set measurements. Apart fromthis, in order to solve the accuracy problem caused by using Gauss model, theproposed method calculates the link loss rate by the combination of solving linearequations and Gibbs sample. Simulation results show that the proposed method can obtain a higher accuracy with less end-to-end measurement paths.In the discrete delay model, the fewer number of basic computational unit forend-to-end path delay decomposition, the faster link delay distribution calculationthere will be. In order to speed up the inference methods of the link delaydistribution based on discrete delay model, and resolve the mutual restraint inaccuracy and time of calculate, a fast delay inference method based on hierarchydecomposition is proposed. This method decomposes the end-to-end path delay intosubtree units by the levels of the topology, and calculates the link delay distributionbased on those subtree units. Because the number of basic computational unit isfewer than that decomposed into link delay units, it can speed up the process of theinference of the link delay distribution. Simulation results show that the proposedmethod can improve the efficiency of the link delay distribution without losing theaccuracy of the EM algorithm.In order to improve the accuracy and efficiency of the topology inferencealgorithm for unicast network, an efficient and adaptive topology inference methodis proposed. With the information of TTL hop count, this method reduces thenumber of the probe pairs needed in the process of bisection Depth-First SearchOrdering, and improves the efficiency of the topology inference. In addition,through the analysis of the principle of the Depth-First Search topology inferencealgorithm, a sufficient condition for the algorithm to return the correct networktopology is given. Based on this condition, an adaptive threshold selection method isproposed, which can improve the accuracy of the topology inference when thenetwork link parameters are unknown. Simulation results show that the proposedmethod can obtain a higher accuracy and efficiency.