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A Method for Overlay Network Latency Estimation from Previous Observation

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dc.contributor.author Sun, Weihua en
dc.contributor.author Shibata, Naoki en
dc.contributor.author Yasumoto, Keiichi en
dc.contributor.author Mori, Masaaki en
dc.date.accessioned 2017-01-10T07:31:56Z en
dc.date.available 2017-01-10T07:31:56Z en
dc.date.issued 2013 en
dc.identifier.isbn 9781612082455 en
dc.identifier.uri http://hdl.handle.net/10061/11378 en
dc.description ICN'2013 : the Twelfth International Conference on Networks , Jan 27-Feb 1, 2013 , Seville, Spain en
dc.description.abstract Estimation of the qualities of overlay links is useful for optimizing overlay networks on the Internet. Existing estimation methods requires sending large quantities of probe packets between two nodes, and the software for measurements have to be executed at both of the end nodes. Accurate measurements require many probe packets to be sent, and other communication can be disrupted by significantly increased network traffic. In this paper, we propose a link quality estimation method based on supervised learning from the previous observation of other similar links. Our method does not need to exchange probe packets, estimation can be quickly made to know qualities of many overlay links without wasting bandwidth and processing time on many nodes. We conducted evaluation of our method on PlanetLab, and our method showed better performance on path latency estimation than estimating results from geographical distance between the two end nodes. en
dc.language.iso en en
dc.publisher IARIA en
dc.relation.ispartof http://www.thinkmind.org/download_full.php?instance=ICN+2013 en
dc.rights Copyright c 2013 IARIA en
dc.subject quality en
dc.subject PlanetLab en
dc.subject Estimation en
dc.subject Learning Algorithm en
dc.title A Method for Overlay Network Latency Estimation from Previous Observation en
dc.type.nii Conference Paper en
dc.textversion Publisher en
dc.identifier.spage 95 en
dc.identifier.epage 100 en
dc.identifier.NAIST-ID 22740047 en
dc.identifier.NAIST-ID 73292559 en

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