{"id":"https://openalex.org/W2984400118","doi":"https://doi.org/10.1109/ccnc46108.2020.9045116","title":"When Deep Learning meets Web Measurements to infer Network Performance","display_name":"When Deep Learning meets Web Measurements to infer Network Performance","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W2984400118","doi":"https://doi.org/10.1109/ccnc46108.2020.9045116","mag":"2984400118"},"language":"en","primary_location":{"id":"doi:10.1109/ccnc46108.2020.9045116","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccnc46108.2020.9045116","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 17th Annual Consumer Communications &amp; Networking Conference (CCNC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044271765","display_name":"Imane Taibi","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I56067802","display_name":"Universit\u00e9 de Rennes","ror":"https://ror.org/015m7wh34","country_code":"FR","type":"education","lineage":["https://openalex.org/I56067802"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Imane Taibi","raw_affiliation_strings":["Universit\u00e9 de Rennes 1, Inria, CNRS, France","DIANA - Design, Implementation and Analysis of Networking Architectures (2004 route des Lucioles BP 93 F-06902 Sophia Antipolis (France) - France)","DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS (Campus de Beaulieu 35042 Rennes cedex - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 de Rennes 1, Inria, CNRS, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I56067802"]},{"raw_affiliation_string":"DIANA - Design, Implementation and Analysis of Networking Architectures (2004 route des Lucioles BP 93 F-06902 Sophia Antipolis (France) - France)","institution_ids":[]},{"raw_affiliation_string":"DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS (Campus de Beaulieu 35042 Rennes cedex - France)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020555624","display_name":"Yassine Hadjadj\u2010Aoul","orcid":"https://orcid.org/0000-0003-4864-4609"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I56067802","display_name":"Universit\u00e9 de Rennes","ror":"https://ror.org/015m7wh34","country_code":"FR","type":"education","lineage":["https://openalex.org/I56067802"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Yassine Hadjadj-Aoul","raw_affiliation_strings":["Universit\u00e9 de Rennes 1, Inria, CNRS, France","DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS (Campus de Beaulieu 35042 Rennes cedex - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 de Rennes 1, Inria, CNRS, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I56067802"]},{"raw_affiliation_string":"DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS (Campus de Beaulieu 35042 Rennes cedex - France)","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056895632","display_name":"Chadi Barakat","orcid":"https://orcid.org/0000-0003-2044-1279"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chadi Barakat","raw_affiliation_strings":["Universit\u00e9 C\u00f4te d'Azur, Inria, France","DIANA - Design, Implementation and Analysis of Networking Architectures (2004 route des Lucioles BP 93 F-06902 Sophia Antipolis (France) - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 C\u00f4te d'Azur, Inria, France","institution_ids":[]},{"raw_affiliation_string":"DIANA - Design, Implementation and Analysis of Networking Architectures (2004 route des Lucioles BP 93 F-06902 Sophia Antipolis (France) - France)","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10138","display_name":"Network Traffic and Congestion Control","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11478","display_name":"Caching and Content Delivery","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8444766998291016},{"id":"https://openalex.org/keywords/troubleshooting","display_name":"Troubleshooting","score":0.6677230000495911},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6307724118232727},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6161602139472961},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6034638285636902},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6027109622955322},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5361655950546265},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5344829559326172},{"id":"https://openalex.org/keywords/the-internet","display_name":"The Internet","score":0.5022127628326416},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4828201234340668},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.45877325534820557},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38772499561309814},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.147201269865036}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8444766998291016},{"id":"https://openalex.org/C147494362","wikidata":"https://www.wikidata.org/wiki/Q2078905","display_name":"Troubleshooting","level":2,"score":0.6677230000495911},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6307724118232727},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6161602139472961},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6034638285636902},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6027109622955322},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5361655950546265},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5344829559326172},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.5022127628326416},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4828201234340668},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.45877325534820557},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38772499561309814},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.147201269865036},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ccnc46108.2020.9045116","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccnc46108.2020.9045116","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 17th Annual Consumer Communications &amp; Networking Conference (CCNC)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-02358004v1","is_oa":false,"landing_page_url":"https://inria.hal.science/hal-02358004","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://ccnc2020.ieee-ccnc.org/","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","score":0.46000000834465027,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1803485664","https://openalex.org/W1987962850","https://openalex.org/W1995950297","https://openalex.org/W2022359415","https://openalex.org/W2136876370","https://openalex.org/W2165723722","https://openalex.org/W2464900811","https://openalex.org/W2471925936","https://openalex.org/W2559093073","https://openalex.org/W2588553759","https://openalex.org/W2606791310","https://openalex.org/W2736772308","https://openalex.org/W2738946621","https://openalex.org/W2782913205","https://openalex.org/W2807940212","https://openalex.org/W2898349265","https://openalex.org/W3007940350"],"related_works":["https://openalex.org/W3013479934","https://openalex.org/W4210597238","https://openalex.org/W4318325534","https://openalex.org/W4206476896","https://openalex.org/W4240398146","https://openalex.org/W2950310564","https://openalex.org/W4238338086","https://openalex.org/W2467308209","https://openalex.org/W2913357653","https://openalex.org/W1981753479"],"abstract_inverted_index":{"Web":[0,127],"browsing":[1],"remains":[2],"one":[3],"of":[4,8,35,65,71,102,170],"the":[5,9,32,82,100,126,139,187],"dominant":[6],"applications":[7],"internet,":[10],"so":[11,25],"inferring":[12],"network":[13,44,91,120,141],"performance":[14,92],"becomes":[15],"crucial":[16],"for":[17],"both":[18],"users":[19],"and":[20,23,84,96,125,164],"providers":[21],"(access":[22],"content)":[24],"as":[26,151],"to":[27,30,75,89,134,154,182],"be":[28],"able":[29],"identify":[31],"root":[33],"cause":[34],"any":[36],"service":[37],"degradation.":[38],"Recent":[39],"works":[40],"have":[41,177],"proposed":[42],"several":[43],"troubleshooting":[45],"tools,":[46],"e.g,":[47],"NDT,":[48],"MobiPerf,":[49],"SpeedTest,":[50],"Fathom.":[51],"Yet,":[52],"these":[53],"tools":[54],"are":[55,122],"either":[56],"computationally":[57],"expensive,":[58],"less":[59],"generic":[60],"or":[61],"greedy":[62],"in":[63,81],"terms":[64],"data":[66],"consumption.":[67],"The":[68],"main":[69],"purpose":[70],"this":[72,108],"work":[73],"is":[74,128,132],"leverage":[76],"passive":[77],"measurements":[78],"freely":[79],"available":[80],"browser":[83],"machine":[85],"learning":[86,156],"techniques":[87,149],"(ML)":[88],"infer":[90],"(e.g.,":[93],"delay,":[94],"bandwidth":[95],"loss":[97],"rate)":[98],"without":[99],"addition":[101],"new":[103],"measurement":[104],"overhead.":[105],"To":[106],"enable":[107],"inference,":[109],"we":[110,145],"propose":[111],"a":[112,178],"framework":[113],"based":[114],"on":[115],"extensive":[116],"controlled":[117],"experiments":[118,172],"where":[119],"configurations":[121],"artificially":[123],"varied":[124],"browsed,":[129],"then":[130],"ML":[131,148,184],"applied":[133],"build":[135],"models":[136,157],"that":[137,174],"estimate":[138],"underlying":[140],"performance.":[142],"In":[143],"particular,":[144],"contrast":[146],"classical":[147,183],"(such":[150],"random":[152],"forest)":[153],"deep":[155],"trained":[158],"using":[159,192],"fully":[160],"connected":[161],"neural":[162,166,175],"networks":[163,167,176],"convolutional":[165],"(CNN).":[168],"Results":[169],"our":[171],"show":[173],"higher":[179],"accuracy":[180,189],"compared":[181],"approaches.":[185],"Furthermore,":[186],"model":[188],"improves":[190],"considerably":[191],"CNN.":[193]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-25T15:57:00.446498","created_date":"2025-10-10T00:00:00"}
