Randomized algorithms
A few logs suffice to build (almost) all trees (l): part I
Random Structures & Algorithms
Multicast-based inference of network-internal delay distributions
IEEE/ACM Transactions on Networking (TON)
On the complexity of distance-based evolutionary tree reconstruction
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Distorted Metrics on Trees and Phylogenetic Forests
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Phylogenies without Branch Bounds: Contracting the Short, Pruning the Deep
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
Multicast-based inference of network-internal loss characteristics
IEEE Transactions on Information Theory
Topology discovery of sparse random graphs with few participants
Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Topology discovery of sparse random graphs with few participants
ACM SIGMETRICS Performance Evaluation Review - Performance evaluation review
Learning Latent Tree Graphical Models
The Journal of Machine Learning Research
| Hi-index | 0.00 |
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. © 2010 Wiley Periodicals, Inc. Random Struct. Alg., 2010