Reference : Rating Network Paths for Locality-Aware Overlay Construction and Routing
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/170423
Rating Network Paths for Locality-Aware Overlay Construction and Routing
English
Du, Wei [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Réseaux informatiques > > > > >]
Liao, Yongjun [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Réseaux informatiques >]
Tao, Narisu [University of Göttingen > Computer Networks > > >]
Geurts, Pierre [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique >]
Fu, Xiaoming [University of Göttingen > Computer Networks > > > > > > >]
Leduc, Guy mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Réseaux informatiques >]
Oct-2015
IEEE/ACM Transactions on Networking
Institute of Electrical and Electronics Engineers
23
5
1661-1673
Yes (verified by ORBi)
International
1063-6692
1558-2566
New York
NY
[en] rating-based network measurement ; recommender system ; matrix factorization ; network inference
[en] This paper investigates the rating of network paths,
i.e. acquiring quantized measures of path properties such as
round-trip time and available bandwidth. Comparing to finegrained
measurements, coarse-grained ratings are appealing in
that they are not only informative but also cheap to obtain.
Motivated by this insight, we firstly address the scalable
acquisition of path ratings by statistical inference. By observing
similarities to recommender systems, we examine the applicability
of solutions to recommender system and show that our
inference problem can be solved by a class of matrix factorization
techniques. A technical contribution is an active and progressive
inference framework that not only improves the accuracy by
selectively measuring more informative paths but also speeds
up the convergence for available bandwidth by incorporating its
measurement methodology.
Then, we investigate the usability of rating-based network
measurement and inference in applications. A case study is
performed on whether locality awareness can be achieved for
overlay networks of Pastry and BitTorrent using inferred ratings.
We show that such coarse-grained knowledge can improve the
performance of peer selection and that finer granularities do not
always lead to larger improvements.
Researchers ; Professionals
http://hdl.handle.net/2268/170423
10.1109/TNET.2014.2337371
FP7 ; 318627 - MPLANE - mPlane – an Intelligent Measurement Plane for Future Network and Application Management

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