Results 1  10
of
10
Network tomography: A review and recent developments
 In Fan and Koul, editors, Frontiers in Statistics
, 2006
"... 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 largescale inverse problems. The first deals with passive tomography where aggregate data ar ..."
Abstract

Cited by 19 (5 self)
 Add to MetaCart
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 largescale 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 pathlevel information. The main problem of interest here is the estimation of the origindestination traffic matrix. The second, referred to as active tomography, deals with reconstructing linklevel information from endtoend pathlevel measurements obtained by actively probing the network. The primary application in this case is estimation of qualityofservice 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.
Understanding the topology of a telephone network via internallysensed network tomography
 In Proc. IEEE International Confernece on Acoustics, Speech, and Signal Processing
, 2005
"... The ability to determine the topology of worldwide telephone networks offers the promise of substantially improving their operating efficiency. This paper explores the problem of identifying the topology of a telephone network using observations made within the network. Using tomographic methods i ..."
Abstract

Cited by 7 (5 self)
 Add to MetaCart
(Show Context)
The ability to determine the topology of worldwide telephone networks offers the promise of substantially improving their operating efficiency. This paper explores the problem of identifying the topology of a telephone network using observations made within the network. Using tomographic methods inspired by medical imaging, we consider measurements made by transmitting probes (e.g., phone calls) between network endpoints. In general, these measurements alone do not suffice to reconstruct a unique network, and in fact, there are many network topologies from which the set of measurements could have been generated. We propose a topology reconstruction algorithm based on correlating measurements collected at different internal nodes, and identify conditions under which correctness of the inferred topology is guaranteed. 1. BACKGROUND
Hierarchical Inference of Unicast Network Topologies Based on EndtoEnd Measurements
"... Abstract—In this paper, we address the problem of topology discovery in unicast logical tree networks using endtoend measurements. Without any cooperation from the internal routers, topology estimation can be formulated as hierarchical clustering of the leaf nodes based on pairwise correlations as ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
Abstract—In this paper, we address the problem of topology discovery in unicast logical tree networks using endtoend measurements. Without any cooperation from the internal routers, topology estimation can be formulated as hierarchical clustering of the leaf nodes based on pairwise correlations as similarity metrics. Unlike previous work that first assumes the network topology is a binary tree and then tries to generalize to a nonbinary tree, we provide a framework that directly deals with general logical tree topologies. A hierarchical algorithm to estimate the topology is developed in a recursive manner by finding the best partitions of the leaf nodes level by level. Our simulations show that the algorithm is more robust than binarytree based methods. Index Terms—Graphbased clustering, mixture models, network tomography, topology estimation. I.
Multiple Source Internet Tomography
"... Abstract — Information about the topology and linklevel characteristics of a network is critical for many applications including network diagnostics and management. However, this information is not always directly accessible; subnetworks may not cooperate in releasing information and widespread loc ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
Abstract — Information about the topology and linklevel characteristics of a network is critical for many applications including network diagnostics and management. However, this information is not always directly accessible; subnetworks may not cooperate in releasing information and widespread local measurement can be prohibitively expensive. Network tomographic techniques obviate the need for network cooperation, but the majority assume probing from a single source, which imposes scalability limitations because sampling traffic is concentrated on network links close to the source. We describe a multiple source, endtoend sampling architecture that uses coordinated transmission of carefully engineered multipacket probes to jointly infer logical topology and estimate linklevel performance characteristics. We commence by demonstrating that the general multiple source, multiple destination tomography problem can be formally reduced to the two source, two destination case, allowing the immediate generalization of any sampling techniques developed for the simpler, smaller scenario. We then describe a method for testing whether links are shared in the topologies perceived by individual sources, and describe how to fuse the measurements in the shared case to generate more accurate estimates of the linklevel performance statistics. Index Terms — Internet tomography, endtoend measurements, active probing, topology discovery, loss rate estimation.
Topology Discovery on Unicast Networks: A Hierarchical Approach Based on EndtoEnd Measurements
, 2005
"... In this paper we address the problem of topology discovery in unicast logical tree networks using endtoend measurements. Without any cooperation from the internal routers, topology estimation can be formulated as hierarchical clustering of the leaf nodes based on pairwise correlations as similarit ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
(Show Context)
In this paper we address the problem of topology discovery in unicast logical tree networks using endtoend measurements. Without any cooperation from the internal routers, topology estimation can be formulated as hierarchical clustering of the leaf nodes based on pairwise correlations as similarity metrics. We investigate three types of similarity metrics: queueing delay measured by sandwich probes, delay variance measured by packet pairs, and loss rate measured also by packet pairs. Unlike previous work which first assumes the network topology is a binary tree and then tries to generalize to a nonbinary tree, we provide a framework which directly deals with general logical tree topologies. Based on our proposed finite mixture model for the set of similarity measurements we develop a penalized hierarchical topology likelihood that leads to a natural clustering of the leaf nodes level by level. A hierarchical algorithm to estimate the topology is developed in a similar manner by finding the best partitions of the leaf nodes. Our simulations show that the algorithm is more robust than binarytree based methods. The three types of similarity metrics are also evaluated under various network load conditions using ns2. 1
1 Multiple Source Internet Tomography
"... Abstract — Information about the topology and linklevel characteristics of a network is critical for many applications including network diagnostics and management. However, this information is not always directly accessible; subnetworks may not cooperate in releasing information and widespread loc ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract — Information about the topology and linklevel characteristics of a network is critical for many applications including network diagnostics and management. However, this information is not always directly accessible; subnetworks may not cooperate in releasing information and widespread local measurement can be prohibitively expensive. Network tomographic techniques obviate the need for network cooperation, but the majority assume probing from a single source, which imposes scalability limitations because sampling traffic is concentrated on network links close to the source. We describe a multiple source, endtoend sampling architecture that uses coordinated transmission of carefully engineered multipacket probes to jointly infer logical topology and estimate linklevel performance characteristics. We commence by demonstrating that the general multiple source, multiple destination tomography problem can be formally reduced to the two source, two destination case, allowing the immediate generalization of any sampling techniques developed for the simpler, smaller scenario. We then describe a method for testing whether links are shared in the topologies perceived by individual sources, and describe how to fuse the measurements in the shared case to generate more accurate estimates of the linklevel performance statistics. Index Terms — Internet tomography, endtoend measurements, active probing, topology discovery, loss rate estimation.
NETWORK TOMOGRAPHY: A REVIEW AND RECENT DEVELOPMENTS
"... 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 largescale inverse problems. The first deals with passive tomography where aggregate data ar ..."
Abstract
 Add to MetaCart
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 largescale 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 pathlevel information. The main problem of interest here is the estimation of the origindestination traffic matrix. The second, referred to as active tomography, deals with reconstructing linklevel information from endtoend pathlevel measurements obtained by actively probing the network. The primary application in this case is estimation of qualityofservice 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.
Doctoral Committee:
, 2004
"... To my parents ii ACKNOWLEDGEMENTS I am greatly indebted to my advisors, Professor George Michailidis and Professor Vijayan N. Nair, for guiding me through my Ph.D program. I have learned a lot from them. This will help me throughout my career. I would also like to thank my committee members, Profess ..."
Abstract
 Add to MetaCart
(Show Context)
To my parents ii ACKNOWLEDGEMENTS I am greatly indebted to my advisors, Professor George Michailidis and Professor Vijayan N. Nair, for guiding me through my Ph.D program. I have learned a lot from them. This will help me throughout my career. I would also like to thank my committee members, Professors Kerby Shedden and Jeffrey A. Fessler. I am especially grateful to Professor Shedden for answering many questions on programming and computations, and for his valuable advice. Last but not least, I thank my parents for their unwavering support during these five years. I feel fortunate that I followed their advice to pursue a Ph.D degree five years ago.
NETWORK TOMOGRAPHY: A REVIEW AND RECENT DEVELOPMENTS
"... 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 largescale inverse problems. The first deals with passive tomography where aggregate data a ..."
Abstract
 Add to MetaCart
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 largescale 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 pathlevel information. The main problem of interest here is the estimation of the origindestination traffic matrix. The second, referred to as active tomography, deals with reconstructing linklevel information from endtoend pathlevel measurements obtained by actively probing the network. The primary application in this case is estimation of qualityofservice 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.
Doctoral Committee:
, 2005
"... To Jess, thanks for waiting. ii ACKNOWLEDGEMENTS I am deeply indebted to my advisors George and Vijay. In addition to providing me with knowledge, they are most responsible for keeping me motivated and making me live up to my potential as much as possible. I take sole responsibility for any remainin ..."
Abstract
 Add to MetaCart
To Jess, thanks for waiting. ii ACKNOWLEDGEMENTS I am deeply indebted to my advisors George and Vijay. In addition to providing me with knowledge, they are most responsible for keeping me motivated and making me live up to my potential as much as possible. I take sole responsibility for any remaining unused potential. Thank you also to the rest of my committee, Kerby Shedden and Anna Gilbert, for their helpful comments and suggestions. More thanks than I can put down on paper go to my Uncle Les and Aunt Deb. It’s hard to imagine I would have turned out this well if it weren’t for them. My sister Jenny and cousins Carrie, Aaron, Laura, Mike, and Mary also contributed by putting up with me for all these years. Thank you also to the rest of my family. Earl Lawrence plays Fender and Yamaha guitars and uses Ernie Ball and GHS strings (this is as close as I’ll ever get to having liner notes).