Large-scale network analytics
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Scalable and accurate analysis of networks is essential to a wide variety of existing and emerging network systems. Specifically, network measurement and analysis helps to understand networks, improve existing services, and enable new data-mining applications. To support various services and applications in large-scale networks, network analytics must address the following challenges: (i) how to conduct scalable analysis in networks with a large number of nodes and links, (ii) how to flexibly accommodate various objectives from different administrative tasks, (iii) and how to cope with the dynamic changes in the networks. This dissertation presents novel path analysis schemes that effectively address the above challenges in analyzing pair-wise relationships among networked entities. In doing so, we make the following three major contributions to large-scale IP networks, social networks, and application service networks. For IP networks, we propose an accurate and flexible framework for path property monitoring. Analyzing the performance side of paths between pairs of nodes, our framework incorporates approaches that perform exact reconstruction of path properties as well as approximate reconstruction. Our framework is highly scalable to design measurement experiments that span thousands of routers and end hosts. It is also flexible to accommodate a variety of design requirements. For social networks, we present scalable and accurate graph embedding schemes. Aimed at analyzing the pair-wise relationships of social network users, we present three dimensionality reduction schemes leveraging matrix factorization, count-min sketch, and graph clustering paired with spectral graph embedding. As concrete applications showing the practical value of our schemes, we apply them to the important social analysis tasks of proximity estimation, missing link inference, and link prediction. The results clearly demonstrate the accuracy, scalability, and flexibility of our schemes for analyzing social networks with millions of nodes and tens of millions of links. For application service networks, we provide a proactive service quality assessment scheme. Analyzing the relationship between the satisfaction level of subscribers of an IPTV service and network performance indicators, our proposed scheme proactively (i.e., detect issues before IPTV subscribers complain) assesses user-perceived service quality using performance metrics collected from the network. From our evaluation using network data collected from a commercial IPTV service provider, we show that our scheme is able to predict 60% of the service problems that are complained by customers with only 0.1% of false positives.
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