Guanjue Wang

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We address the problem of inferring link loss rates from unicast end-to-end measurements. Different from previous tomographic techniques, we provide a method to partition all links in the network into several subsets-loss inferences can be performed independently among each subset. We also design a approach, based on the independence of links, to infer the(More)
In this paper, we introduce a network performance anomaly detection and localization method based on active probing, aiming at avoiding waste of unnecessary probes and reducing detecting time by decreasing selecting rounds in detection phase. We propose a method of classifying detection strategies in order to find a balance between extra calculation and(More)
We suggest a method of bottleneck diagnosis with more excellent performance. There are some problems with current diagnosis methods, such as extra network packets and sensitivity to time changes. In this paper, for UDP network, we propose a new method of bottleneck diagnosis based on the concept of network utility maximization. This bottleneck diagnosis(More)
An approach for link loss inference on large networks is proposed in this paper regarding with the cost of measurements. The measurements on large-scale networks usually cost much and the diagnosis of bottleneck on these networks are expensive and inefficient. We adapt Bayesian experimental design for measurement-path selecting with the total cost(More)
Decision tree is an important tool in decision analysis. In this paper, we propose the optimized ID3 Algorithm and three principles for pruning algorithm in different cases. Based on the Scilab platform, the application in a personal credit issue performs better than the traditional method. Besides, the difference between the decision tree and rough set is(More)
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