End-to-end quality of service seen by applications : a statistical learning approach
Resumen:
The focus of this work is on the estimation of quality of servi ce (QoS) parameters seen by an application. Our proposal is based on end-to-end active measurements and sta tistical learning tools. We propose a methodology where the system is trained during short periods with application flows and probe packets bursts. We learn the relation be- tween QoS parameters seen by the application and the state of the network path, which is inferred from the interarrival times of the probe packets bursts. We obtain a continuous non intrusive QoS monitoring methodology. We propose two di ff erent estimators of the network state and analyze them using Nadaraya-Watson estimator and Support Vector Machines (SVM) for regression. We compare these approaches and we show results obtained by simulations and by measures in operational networks
| 2010 | |
|
End-to-end active measurements Statistical learning Nadaraya-Watson Support Vector Machines QoS Telecomunicaciones |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/38692 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522992193798144 |
|---|---|
| author | Belzarena, Pablo |
| author2 | Aspirot, Laura |
| author2_role | author |
| author_facet | Belzarena, Pablo Aspirot, Laura |
| author_role | author |
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| bitstream.checksumAlgorithm.fl_str_mv | MD5 MD5 MD5 MD5 MD5 |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Belzarena, Pablo Aspirot, Laura |
| dc.date.accessioned.none.fl_str_mv | 2023-08-01T20:33:22Z |
| dc.date.available.none.fl_str_mv | 2023-08-01T20:33:22Z |
| dc.date.issued.es.fl_str_mv | 2010 |
| dc.date.submitted.es.fl_str_mv | 20230801 |
| dc.description.abstract.none.fl_txt_mv | The focus of this work is on the estimation of quality of servi ce (QoS) parameters seen by an application. Our proposal is based on end-to-end active measurements and sta tistical learning tools. We propose a methodology where the system is trained during short periods with application flows and probe packets bursts. We learn the relation be- tween QoS parameters seen by the application and the state of the network path, which is inferred from the interarrival times of the probe packets bursts. We obtain a continuous non intrusive QoS monitoring methodology. We propose two di ff erent estimators of the network state and analyze them using Nadaraya-Watson estimator and Support Vector Machines (SVM) for regression. We compare these approaches and we show results obtained by simulations and by measures in operational networks |
| dc.identifier.citation.es.fl_str_mv | Belzarena, P., Aspirot, L. End-to-end quality of service seen by applications : a statistical learning approach [Preprint] Publicado en Computer Networks, 2010, v. 54, no. 17. https://doi.org/10.1016/j.comnet.2010.06.004. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/38692 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | End-to-end active measurements Statistical learning Nadaraya-Watson Support Vector Machines QoS |
| dc.subject.other.es.fl_str_mv | Telecomunicaciones |
| dc.title.none.fl_str_mv | End-to-end quality of service seen by applications : a statistical learning approach |
| dc.type.es.fl_str_mv | Preprint |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/preprint |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/submittedVersion |
| description | The focus of this work is on the estimation of quality of servi ce (QoS) parameters seen by an application. Our proposal is based on end-to-end active measurements and sta tistical learning tools. We propose a methodology where the system is trained during short periods with application flows and probe packets bursts. We learn the relation be- tween QoS parameters seen by the application and the state of the network path, which is inferred from the interarrival times of the probe packets bursts. We obtain a continuous non intrusive QoS monitoring methodology. We propose two di ff erent estimators of the network state and analyze them using Nadaraya-Watson estimator and Support Vector Machines (SVM) for regression. We compare these approaches and we show results obtained by simulations and by measures in operational networks |
| eu_rights_str_mv | openAccess |
| format | preprint |
| id | COLIBRI_866a09d71c3c54c9c49497c5ce231797 |
| identifier_str_mv | Belzarena, P., Aspirot, L. End-to-end quality of service seen by applications : a statistical learning approach [Preprint] Publicado en Computer Networks, 2010, v. 54, no. 17. https://doi.org/10.1016/j.comnet.2010.06.004. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/38692 |
| publishDate | 2010 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | 2023-08-01T20:33:22Z2023-08-01T20:33:22Z201020230801Belzarena, P., Aspirot, L. End-to-end quality of service seen by applications : a statistical learning approach [Preprint] Publicado en Computer Networks, 2010, v. 54, no. 17. https://doi.org/10.1016/j.comnet.2010.06.004.https://hdl.handle.net/20.500.12008/38692The focus of this work is on the estimation of quality of servi ce (QoS) parameters seen by an application. Our proposal is based on end-to-end active measurements and sta tistical learning tools. We propose a methodology where the system is trained during short periods with application flows and probe packets bursts. We learn the relation be- tween QoS parameters seen by the application and the state of the network path, which is inferred from the interarrival times of the probe packets bursts. We obtain a continuous non intrusive QoS monitoring methodology. We propose two di ff erent estimators of the network state and analyze them using Nadaraya-Watson estimator and Support Vector Machines (SVM) for regression. We compare these approaches and we show results obtained by simulations and by measures in operational networksMade available in DSpace on 2023-08-01T20:33:22Z (GMT). No. of bitstreams: 5 BA10.pdf: 961143 bytes, checksum: 53e3ea5269f0711d0d16aae869402465 (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4194 bytes, checksum: 7f2e2c17ef6585de66da58d1bfa8b5e1 (MD5) Previous issue date: 2010enengLas obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)End-to-end active measurementsStatistical learningNadaraya-WatsonSupport Vector MachinesQoSTelecomunicacionesEnd-to-end quality of service seen by applications : a statistical learning approachPreprintinfo:eu-repo/semantics/preprintinfo:eu-repo/semantics/submittedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaBelzarena, PabloAspirot, LauraTelecomunicacionesAnálisis de Redes, Tráfico y Estadísticas de 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- Universidad de la Repúblicafalse |
| spellingShingle | End-to-end quality of service seen by applications : a statistical learning approach Belzarena, Pablo End-to-end active measurements Statistical learning Nadaraya-Watson Support Vector Machines QoS Telecomunicaciones |
| status_str | submittedVersion |
| title | End-to-end quality of service seen by applications : a statistical learning approach |
| title_full | End-to-end quality of service seen by applications : a statistical learning approach |
| title_fullStr | End-to-end quality of service seen by applications : a statistical learning approach |
| title_full_unstemmed | End-to-end quality of service seen by applications : a statistical learning approach |
| title_short | End-to-end quality of service seen by applications : a statistical learning approach |
| title_sort | End-to-end quality of service seen by applications : a statistical learning approach |
| topic | End-to-end active measurements Statistical learning Nadaraya-Watson Support Vector Machines QoS Telecomunicaciones |
| url | https://hdl.handle.net/20.500.12008/38692 |
