Accurate and efficient SLA compliance monitoring
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Network loss inference with second order statistics of end-to-end flows
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
NetDiagnoser: troubleshooting network unreachabilities using end-to-end probes and routing data
CoNEXT '07 Proceedings of the 2007 ACM CoNEXT conference
Probabilistic inference of lossy links using end-to-end data in sensor networks
CoNEXT '07 Proceedings of the 2007 ACM CoNEXT conference
Practical issues with using network tomography for fault diagnosis
ACM SIGCOMM Computer Communication Review
Measurement methods for fast and accurate blackhole identification with binary tomography
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
Conditions for a unique non-negative solution to an underdetermined system
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Multiobjective monitoring for SLA compliance
IEEE/ACM Transactions on Networking (TON)
Netscope: practical network loss tomography
INFOCOM'10 Proceedings of the 29th conference on Information communications
California fault lines: understanding the causes and impact of network failures
Proceedings of the ACM SIGCOMM 2010 conference
Network tomography on correlated links
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Network link tomography and compressive sensing
Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Network link tomography and compressive sensing
ACM SIGMETRICS Performance Evaluation Review - Performance evaluation review
Model-based identification of dominant congested links
IEEE/ACM Transactions on Networking (TON)
Shifting network tomography toward a practical goal
Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies
Sparsity without the complexity: loss localisation using tree measurements
IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part I
Proceedings of the 2012 ACM conference on Internet measurement conference
Automatic test packet generation
Proceedings of the 8th international conference on Emerging networking experiments and technologies
Toward accurate and practical network tomography
ACM SIGOPS Operating Systems Review
A Two-Stage Approach for Network Monitoring
Journal of Network and Systems Management
Efficient Loss Inference Algorithm Using Unicast End-to-End Measurements
Journal of Network and Systems Management
The design space of probing algorithms for network-performance measurement
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
Fine-grain diagnosis of overlay performance anomalies using end-point network experiences
Proceedings of the 8th International Conference on Network and Service Management
A comparison of syslog and IS-IS for network failure analysis
Proceedings of the 2013 conference on Internet measurement conference
| Hi-index | 754.84 |
In network performance tomography, characteristics of the network interior, such as link loss and packet latency, are inferred from correlated end-to-end measurements. Most work to date is based on exploiting packet level correlations, e.g., of multicast packets or unicast emulations of them. However, these methods are often limited in scope-multicast is not widely deployed-or require deployment of additional hardware or software infrastructure. Some recent work has been successful in reaching a less detailed goal: identifying the lossiest network links using only uncorrelated end-to-end measurements. In this paper, we abstract the properties of network performance that allow this to be done and exploit them with a quick and simple inference algorithm that, with high likelihood, identifies the worst performing links. We give several examples of real network performance measures that exhibit the required properties. Moreover, the algorithm is sufficiently simple that we can analyze its performance explicitly