{"id":"https://openalex.org/W2072940406","doi":"https://doi.org/10.1109/infcom.2013.6566768","title":"Mixture models of endhost network traffic","display_name":"Mixture models of endhost network traffic","publication_year":2013,"publication_date":"2013-04-01","ids":{"openalex":"https://openalex.org/W2072940406","doi":"https://doi.org/10.1109/infcom.2013.6566768","mag":"2072940406"},"language":"en","primary_location":{"id":"doi:10.1109/infcom.2013.6566768","is_oa":false,"landing_page_url":"https://doi.org/10.1109/infcom.2013.6566768","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 Proceedings IEEE INFOCOM","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/A5017404665","display_name":"John Mark Agosta","orcid":"https://orcid.org/0000-0001-9826-6509"},"institutions":[{"id":"https://openalex.org/I1293612202","display_name":"Toyota Motor Corporation (Switzerland)","ror":"https://ror.org/05p0pbv75","country_code":"CH","type":"company","lineage":["https://openalex.org/I1293612202","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]},{"id":"https://openalex.org/I131361393","display_name":"National Institute of Technology, Toyota College","ror":"https://ror.org/01nw25822","country_code":"JP","type":"education","lineage":["https://openalex.org/I131361393"]}],"countries":["CH","JP"],"is_corresponding":false,"raw_author_name":"John Mark Agosta","raw_affiliation_strings":["Toyota Info Technology Center, Inc., Japan","Toyota ITC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Info Technology Center, Inc., Japan","institution_ids":["https://openalex.org/I131361393"]},{"raw_affiliation_string":"Toyota ITC","institution_ids":["https://openalex.org/I1293612202"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089322398","display_name":"Jaideep Chandrashekar","orcid":"https://orcid.org/0000-0003-4615-7487"},"institutions":[{"id":"https://openalex.org/I2929663463","display_name":"Technicolor (Germany)","ror":"https://ror.org/00besvm65","country_code":"DE","type":"company","lineage":["https://openalex.org/I2929663463","https://openalex.org/I4210121266"]},{"id":"https://openalex.org/I4210121266","display_name":"Technicolor (France)","ror":"https://ror.org/02ya5n776","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210121266"]}],"countries":["DE","FR"],"is_corresponding":false,"raw_author_name":"Jaideep Chandrashekar","raw_affiliation_strings":["Technicolor Research Center, France","Technicolor Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technicolor Research Center, France","institution_ids":["https://openalex.org/I4210121266"]},{"raw_affiliation_string":"Technicolor Research","institution_ids":["https://openalex.org/I2929663463"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064525211","display_name":"Mark Crovella","orcid":"https://orcid.org/0000-0002-5005-7019"},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark Crovella","raw_affiliation_strings":["Boston University, USA","Boston University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boston University, USA","institution_ids":["https://openalex.org/I111088046"]},{"raw_affiliation_string":"Boston University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061868730","display_name":"Nina Taft","orcid":null},"institutions":[{"id":"https://openalex.org/I2929663463","display_name":"Technicolor (Germany)","ror":"https://ror.org/00besvm65","country_code":"DE","type":"company","lineage":["https://openalex.org/I2929663463","https://openalex.org/I4210121266"]},{"id":"https://openalex.org/I4210121266","display_name":"Technicolor (France)","ror":"https://ror.org/02ya5n776","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210121266"]}],"countries":["DE","FR"],"is_corresponding":false,"raw_author_name":"Nina Taft","raw_affiliation_strings":["Technicolor Laboratories, France","Technicolor Labs"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technicolor Laboratories, France","institution_ids":["https://openalex.org/I4210121266"]},{"raw_affiliation_string":"Technicolor Labs","institution_ids":["https://openalex.org/I2929663463"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090993570","display_name":"Daniel Ting","orcid":null},"institutions":[{"id":"https://openalex.org/I2252078561","display_name":"Meta (Israel)","ror":"https://ror.org/02388em19","country_code":"IL","type":"company","lineage":["https://openalex.org/I2252078561","https://openalex.org/I4210114444"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Daniel Ting","raw_affiliation_strings":["Facebook"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Facebook","institution_ids":["https://openalex.org/I2252078561"]}]}],"institutions":[],"countries_distinct_count":6,"institutions_distinct_count":6,"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":"2744","issue":null,"first_page":"225","last_page":"229"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9922999739646912,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9902999997138977,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.6364206075668335},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6361725330352783},{"id":"https://openalex.org/keywords/heavy-tailed-distribution","display_name":"Heavy-tailed distribution","score":0.5765780210494995},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5590521097183228},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5208302736282349},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.45664000511169434},{"id":"https://openalex.org/keywords/pareto-distribution","display_name":"Pareto distribution","score":0.4566153883934021},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.44613131880760193},{"id":"https://openalex.org/keywords/exponential-distribution","display_name":"Exponential distribution","score":0.4425638020038605},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.43910884857177734},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.4285692274570465},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42246997356414795},{"id":"https://openalex.org/keywords/exponential-family","display_name":"Exponential family","score":0.41410937905311584},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.2711796760559082},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.267050564289093},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2220357358455658},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.19394809007644653},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18374836444854736},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16651055216789246},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1413048803806305}],"concepts":[{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.6364206075668335},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6361725330352783},{"id":"https://openalex.org/C1602530","wikidata":"https://www.wikidata.org/wiki/Q1472677","display_name":"Heavy-tailed distribution","level":3,"score":0.5765780210494995},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5590521097183228},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5208302736282349},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.45664000511169434},{"id":"https://openalex.org/C190373308","wikidata":"https://www.wikidata.org/wiki/Q837683","display_name":"Pareto distribution","level":2,"score":0.4566153883934021},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.44613131880760193},{"id":"https://openalex.org/C55350006","wikidata":"https://www.wikidata.org/wiki/Q237193","display_name":"Exponential distribution","level":2,"score":0.4425638020038605},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.43910884857177734},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.4285692274570465},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42246997356414795},{"id":"https://openalex.org/C55974624","wikidata":"https://www.wikidata.org/wiki/Q1188504","display_name":"Exponential family","level":2,"score":0.41410937905311584},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2711796760559082},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.267050564289093},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2220357358455658},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.19394809007644653},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18374836444854736},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16651055216789246},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1413048803806305},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/infcom.2013.6566768","is_oa":false,"landing_page_url":"https://doi.org/10.1109/infcom.2013.6566768","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 Proceedings IEEE INFOCOM","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W180653875","https://openalex.org/W602946569","https://openalex.org/W1499963479","https://openalex.org/W1502431891","https://openalex.org/W1506099691","https://openalex.org/W1516506771","https://openalex.org/W1518039036","https://openalex.org/W1543886729","https://openalex.org/W1570233747","https://openalex.org/W1600310449","https://openalex.org/W1753705435","https://openalex.org/W1836055303","https://openalex.org/W2000042664","https://openalex.org/W2025653905","https://openalex.org/W2072940406","https://openalex.org/W2112053513","https://openalex.org/W2119719849","https://openalex.org/W2121407732","https://openalex.org/W2124505612","https://openalex.org/W2128796442","https://openalex.org/W2133985894","https://openalex.org/W2138543759","https://openalex.org/W2139054829","https://openalex.org/W2146625955","https://openalex.org/W2148275477","https://openalex.org/W2167677198","https://openalex.org/W2950517039","https://openalex.org/W2950627632","https://openalex.org/W3103362336","https://openalex.org/W3123239371","https://openalex.org/W3140968660","https://openalex.org/W3163755673","https://openalex.org/W4255582690","https://openalex.org/W4300956553","https://openalex.org/W6635903900","https://openalex.org/W6668786714"],"related_works":["https://openalex.org/W2081133284","https://openalex.org/W2623371928","https://openalex.org/W2035892582","https://openalex.org/W2903098718","https://openalex.org/W2235244790","https://openalex.org/W3178384524","https://openalex.org/W2345245742","https://openalex.org/W2162778980","https://openalex.org/W4226134799","https://openalex.org/W2734666369"],"abstract_inverted_index":{"We":[0,15],"model":[1,19,58,76,90],"a":[2,17,72,84],"little":[3],"studied":[4],"type":[5],"of":[6,20,28,51,66,87,95],"traffic,":[7],"namely":[8],"the":[9,21,42,63,92,97,102,105,110,117,121],"network":[10],"traffic":[11,103],"generated":[12],"from":[13],"endhosts.":[14],"introduce":[16],"parsimonious":[18],"marginal":[22],"distribution":[23],"for":[24,83],"connection":[25],"arrivals":[26],"consisting":[27],"mixture":[29,75],"models":[30],"with":[31],"both":[32],"heavy":[33],"and":[34,54],"light-tailed":[35],"component":[36],"distributions.":[37],"Our":[38,68],"methodology":[39],"assumes":[40],"that":[41,71],"underlying":[43],"user":[44],"data":[45],"can":[46],"be":[47],"fitted":[48],"to":[49,61],"one":[50],"several":[52],"models,":[53],"we":[55],"apply":[56],"Bayesian":[57],"selection":[59],"criterion":[60],"choose":[62],"preferred":[64,78],"combination":[65],"components.":[67,112],"experiments":[69],"show":[70],"simple":[73],"Pareto-exponential":[74],"is":[77],"over":[79],"more":[80],"complex":[81],"alternatives,":[82],"wide":[85,118],"range":[86],"users.":[88],"This":[89],"has":[91],"desirable":[93],"property":[94],"modeling":[96],"entire":[98],"distribution,":[99],"effectively":[100],"clustering":[101],"into":[104],"heavy-tailed":[106],"as":[107,109],"well":[108],"non-heavy-tailed":[111],"Also":[113],"this":[114],"method":[115],"quantifies":[116],"diversity":[119],"in":[120],"observed":[122],"endhost":[123],"traffic.":[124]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2013,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}