Detecting shared congestion of flows via end-to-end measurement
Proceedings of the 2000 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
On network-aware clustering of Web clients
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
Towards global network positioning
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Validating an Access Cost Model for Wide Area Applications
CooplS '01 Proceedings of the 9th International Conference on Cooperative Information Systems
A Precise and Efficient Evaluation of the Proximity Between Web Clients and Their Local DNS Servers
ATEC '02 Proceedings of the General Track of the annual conference on USENIX Annual Technical Conference
Multivariate resource performance forecasting in the network weather service
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Forecasting network performance to support dynamic scheduling using the network weather service
HPDC '97 Proceedings of the 6th IEEE International Symposium on High Performance Distributed Computing
Latency Profiles: Performance Monitoring for Wide Area Applications
WIAPP '03 Proceedings of the The Third IEEE Workshop on Internet Applications
An network measurement architecture for adaptive applications
An network measurement architecture for adaptive applications
AReNA: adaptive distributed catalog infrastructure based on relevance networks
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Statistics for Engineering and the Sciences (5th Edition)
Statistics for Engineering and the Sciences (5th Edition)
Alternative path selection in resilient web infrastructure using performance dependencies
Journal of Web Engineering
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In this paper, we propose a scalable performance management tool for Wide Area Applications. Our objective is to scalably identify non-random associations between pairs of individual Latency Profiles (iLPs) (i.e., latency distributions experienced by clients when connecting to a server) and exploit them in latency prediction. Our approach utilizes Relevance Networks (RNs) to manage tens of thousands of iLPs. Non-random associations between iLPs can be identified by topology-independent measures such as correlation and mutual information. We demonstrate that these non-random associations do indeed have a significant impact in improving the error of latency prediction.