On the constancy of internet path properties
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
The use of end-to-end multicast measurements for characterizing internal network behavior
IEEE Communications Magazine
Network radar: tomography from round trip time measurements
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Monitoring link delays with one measurement host
ACM SIGMETRICS Performance Evaluation Review - Special issue on the First ACM SIGMETRICS Workshop on Large Scale Network Inference (LSNI 2005)
Towards deterministic network diagnosis
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
Towards unbiased end-to-end network diagnosis
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Support for resilient Peer-to-Peer gaming
Computer Networks: The International Journal of Computer and Telecommunications Networking
Identifying lossy links in wired/wireless networks by exploiting sparse characteristics
Computer Networks: The International Journal of Computer and Telecommunications Networking
NetDiagnoser: troubleshooting network unreachabilities using end-to-end probes and routing data
CoNEXT '07 Proceedings of the 2007 ACM CoNEXT conference
Every microsecond counts: tracking fine-grain latencies with a lossy difference aggregator
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Resonance: dynamic access control for enterprise networks
Proceedings of the 1st ACM workshop on Research on enterprise networking
Characterizing VLAN-induced sharing in a campus network
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
mPlane: an architecture for scalable fault localization
Proceedings of the 2009 workshop on Re-architecting the internet
Towards unbiased end-to-end network diagnosis
IEEE/ACM Transactions on Networking (TON)
Two samples are enough: opportunistic flow-level latency estimation using netflow
INFOCOM'10 Proceedings of the 29th conference on Information communications
Proceedings of the ACM SIGCOMM 2010 conference
Enabling flow-level latency measurements across routers in data centers
Hot-ICE'11 Proceedings of the 11th USENIX conference on Hot topics in management of internet, cloud, and enterprise networks and services
Binary versus analogue path monitoring in IP networks
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Practical passive lossy link inference
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Survey on dependable IP over fiber networks
Dependable Systems
Opportunistic flow-level latency estimation using consistent netflow
IEEE/ACM Transactions on Networking (TON)
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Router support for fine-grained latency measurements
IEEE/ACM Transactions on Networking (TON)
On identifying additive link metrics using linearly independent cycles and paths
IEEE/ACM Transactions on Networking (TON)
MAPLE: a scalable architecture for maintaining packet latency measurements
Proceedings of the 2012 ACM conference on Internet measurement conference
Proceedings of the 2012 ACM conference on Internet measurement conference
High-fidelity per-flow delay measurements with reference latency interpolation
IEEE/ACM Transactions on Networking (TON)
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In network performance tomography, characteristics of the network interior are inferred by correlating end-to-end measurements. In much previous work, the presence of correlations must be arranged at the packet level, e.g., using multicast probes or unicast emulations of them. This carries costs in deployment and limits coverage. However, it is difficult to determine performance characteristics without correlations. Some recent work has had success in reaching a lesser goal---identifying the lossiest network links---using only uncorrelated end-to-end measurements. In this paper we abstract the required properties of network performance, and show that they are independent of the particular inference algorithm used. This observation allows us to design a quick and simple inference algorithm that identifies the worst performing link in a badly performing subnetwork, with high likelihood when bad links are uncommon. We give several examples of perforance models and that exhibit the required properties. The performance of the algorithm is analyzed explicitly.