This paper develops a new method for hierarchical clustering. Unlike other existing clustering schemes, our method is based on a generative, tree-structured model that represents relationships between the objects to be clustered, rather than directly modeling properties of objects themselves. In certain problems, this generative model naturally captures the physical mechanisms responsible for relationships among objects, for example, in certain evolutionary tree problems in genetics and communication network topology identification. The paper examines the networking problem in some detail, to illustrate the new clustering method. More broadly, the generative model may not reflect actual physical mechanisms, but it nonetheless provides a means for dealing with errors in the similarity matrix, simultaneously promoting two desirable features in clustering: intra-class similarity and inter-class dissimilarity.

In this article we propose a network topology estimation strategy using unicast end-to-end packet pair delay measurements that is based on mixture models for the delay covariances. An unsupervised learning algorithms is applied to estimate the number of mixture components and delay covariances. The leaf pairs are clustered by a MAP criterion and passed to a hierarchical topology construction algorithm to rebuild the tree. Results from an ns simulation show that our algorithm can identify a network tree with 8 leaf nodes. 1.

The modeling and analysis of computer communications networks give rise to a variety of interesting statistical problems. This paper focuses on network tomography, a term used to characterize two classes of large-scale inverse problems. The first deals with passive tomography where aggregate data are collected at the individual router/node level and the goal is to recover path-level information. The main problem of interest here is the estimation of the origin-destination traffic matrix. The second, referred to as active tomography, deals with reconstructing link-level information from end-to-end path-level measurements obtained by actively probing the network. The primary application in this case is estimation of quality-of-service parameters such as loss rates and delay distributions. The paper provides a review of the statistical issues and developments in network tomography with an emphasis on active tomography. An application to Internet telephony is used to illustrate the results.

This article is an editorial note submitted to CCR. It has NOT been peer reviewed. This paper investigates the practical issues in applying network tomography to monitor failures. We outline an approach for selecting paths to monitor, detecting and confirming the existence of a failure, correlating multiple independent observations into a single failure event, and applying existing binary networking tomography algorithms to identify failures. We evaluate the ability of network tomography algorithms to correctly detect and identify failures in a controlled environment on the VINI testbed.

We use computational phylogenetic techniques to solve a central problem in inferential network monitoring. More precisely, we design a novel algorithm for multicast-based delay inference, that is, the problem of reconstructing delay characteristics of a network from end-to-end delay measurements on network paths. Our inference algorithm is based on additive metric techniques used in phylogenetics. It runs in polynomial time and requires a sample of size only poly(log n). We also show how to recover the topology of the routing tree. 1

Information Technology is creating an exciting time for statistics. In this article, we review the diverse sources of IT data in three clusters: IT core, IT systems, and IT fringe. The new data forms, huge data volumes, and high data speeds of IT are contrasted against the constraints on storage, transmission and computation to point to the challenges and opportunities. In particular, we describe the impacts of IT on a typical statistical investigation of data collection, data visualization, and model fitting, with an emphasis on computation and feature selection.

In this work we address the problem of inferring the presence of a capacity bottleneck from passive measurements in a 3G network. The study is based on one month of packet traces collected in the UMTS core network of mobilkom austria AG & Co KG, the leading mobile telecommunications provider in Austria, EU. During the measurement period a bottleneck link in the UMTS core network was revealed and removed, therefore the traces enable the accurate analysis and comparison of the traffic behavior in the two network conditions: with and without a capacity bottleneck. Two approaches to bottleneck detection are investigated. The first one is based on the signal analysis of the marginal rate distribution of the traffic aggregate along one day cycle. Since TCP-controlled traffic dominates the overall traffic mix, the presence of a bottleneck strains the aggregate rate distribution and compresses it against the capacity limit during the peak hour. The second approach is based on the analysis of several TCP performance parameters, e.g. estimated frequency of

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Inferring the routing topology and link performance from a node to a set of other nodes is an important component in network monitoring and application design. In this paper we propose a general framework for designing topology inference algorithms based on additive metrics. The framework can flexibly fuse information from multiple measurements to achieve better estimation accuracy. We develop computationally efficient (polynomial-time) topology inference algorithms based on the framework. We prove that the probability of correct topology inference of our algorithms converges to one exponentially fast in the number of probing packets. In particular, for applications where nodes may join or leave frequently such as overlay network construction, application-layer multicast, peer-to-peer file sharing/streaming, we propose a novel sequential topology inference algorithm which significantly reduces the probing overhead and can efficiently handle node dynamics. We demonstrate the effectiveness of the proposed inference algorithms via Internet experiments.

Abstract: Active network tomography includes several interesting statistical inverse problems that arise in the context of computer and communication networks. The primary goal in these problems is to recover link-level information about quality-of-service parameters from aggregate end-to-end data measured on paths across the network. The estimation and monitoring of these parameters are of considerable interest to network engineers and Internet service providers. This paper provides a review of the inverse problems and recent research on inference for loss rates and delay distributions. Some new results on parametric inference for delay distributions are developed. The results are illustrated using a network application related to Internet telephony. 1. The Inverse Problems Consider a tree T = {V, E} with a set of nodes V and a set of links or edges E. Figure 1 shows two examples: a simple two-layer symmetric binary tree on the left and a more general four-layer tree on the right. Each member of E is a directed link numbered after the node at its terminus. V includes a root node 0, a set of receiver or destination nodes R and a set of internal nodes I. All transmissions on the tree are initiated at the root node. The internal nodes have a single incoming link and at least two outgoing links (children). The receiver nodes have a single incoming link but no