Reference : Decentralized Prediction of End-to-End Network Performance Classes
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/103128
Decentralized Prediction of End-to-End Network Performance Classes
English
Liao, Yongjun mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Réseaux informatiques >]
Du, Wei [University of Innsbruck (Austria) > > Intelligent and Interactive Systems > >]
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Leduc, Guy mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Réseaux informatiques >]
8-Dec-2011
Proc. of the 7th International Conference on emerging Networking EXperiments and Technologies (CoNEXT)
ACM
Yes
No
International
The 7th International Conference on emerging Networking EXperiments and Technologies (CoNEXT)
6-9 Dec. 2011
Tokyo
Japan
[en] In large-scale networks, full-mesh active probing of end-to-end performance metrics is infeasible. Measuring a small set of pairs and predicting the others is more scalable. Under this framework, we formulate the prediction problem as matrix completion, whereby unknown entries of an incomplete matrix of pairwise measurements are to be predicted. This problem can be solved by matrix factorization because performance matrices have a low rank, thanks to the correlations among measurements. Moreover, its resolution can be fully decentralized without actually building matrices nor relying on special landmarks or central servers. In this paper we demonstrate that this approach is also applicable when the performance values are not measured exactly, but are only known to belong to one among some predefined performance classes, such as "good" and "bad". Such classification-based formulation not only fulfills the requirements of many Internet applications but also reduces the measurement cost and enables a unified treatment of various performance metrics. We propose a decentralized approach based on Stochastic Gradient Descent to solve this class-based matrix completion problem. Experiments on various datasets, relative to two kinds of metrics, show the accuracy of the approach, its robustness against erroneous measurements and its usability on peer selection.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/103128
10.1145/2079296.2079310
FP7 ; 223936 - ECODE - Experimental COgnitive Distributed Engine

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