{"id":"https://openalex.org/W2157458486","doi":"https://doi.org/10.1145/1162678.1162682","title":"SVM learning of IP address structure for latency prediction","display_name":"SVM learning of IP address structure for latency prediction","publication_year":2006,"publication_date":"2006-09-11","ids":{"openalex":"https://openalex.org/W2157458486","doi":"https://doi.org/10.1145/1162678.1162682","mag":"2157458486"},"language":"en","primary_location":{"id":"doi:10.1145/1162678.1162682","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1162678.1162682","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2006 SIGCOMM workshop on Mining network data","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/A5102981350","display_name":"Robert Beverly","orcid":"https://orcid.org/0000-0002-5005-7350"},"institutions":[{"id":"https://openalex.org/I4405258931","display_name":"MIT Computer Science and Artificial Intelligence Laboratory","ror":"https://ror.org/01e85r662","country_code":"US","type":"facility","lineage":["https://openalex.org/I4405258931","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert Beverly","raw_affiliation_strings":["MIT CSAIL"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIT CSAIL","institution_ids":["https://openalex.org/I4405258931"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059317801","display_name":"Karen Sollins","orcid":"https://orcid.org/0000-0003-3686-4065"},"institutions":[{"id":"https://openalex.org/I4405258931","display_name":"MIT Computer Science and Artificial Intelligence Laboratory","ror":"https://ror.org/01e85r662","country_code":"US","type":"facility","lineage":["https://openalex.org/I4405258931","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Karen Sollins","raw_affiliation_strings":["MIT CSAIL"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIT CSAIL","institution_ids":["https://openalex.org/I4405258931"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111658165","display_name":"Arthur Berger","orcid":null},"institutions":[{"id":"https://openalex.org/I10734018","display_name":"Akamai (United States)","ror":"https://ror.org/03tarb191","country_code":"US","type":"company","lineage":["https://openalex.org/I10734018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arthur Berger","raw_affiliation_strings":["MIT/Akamai"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIT/Akamai","institution_ids":["https://openalex.org/I10734018"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.5854,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.91237731,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"299","last_page":"304"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11478","display_name":"Caching and Content Delivery","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T11478","display_name":"Caching and Content Delivery","score":0.9991999864578247,"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/T12326","display_name":"Network Packet Processing and Optimization","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10742","display_name":"Peer-to-Peer Network Technologies","score":0.9922999739646912,"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":0.783977746963501},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6453888416290283},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.5516571998596191},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5140501260757446},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48291489481925964},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07502424716949463}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.783977746963501},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6453888416290283},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.5516571998596191},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5140501260757446},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48291489481925964},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07502424716949463}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1162678.1162682","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1162678.1162682","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2006 SIGCOMM workshop on Mining network data","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.102.1485","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.1485","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.sigcomm.org/sigcomm2006/papers/minenet-04.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1576520375","https://openalex.org/W1604938182","https://openalex.org/W1764289520","https://openalex.org/W1996497509","https://openalex.org/W2012936414","https://openalex.org/W2068593619","https://openalex.org/W2083856268","https://openalex.org/W2093973064","https://openalex.org/W2111102056","https://openalex.org/W2123737232","https://openalex.org/W2129952072","https://openalex.org/W2132051999","https://openalex.org/W2141934596","https://openalex.org/W2144553078","https://openalex.org/W2144901708","https://openalex.org/W2148647281","https://openalex.org/W2156909104","https://openalex.org/W2159910630","https://openalex.org/W2170267557","https://openalex.org/W4241257019","https://openalex.org/W6634442568","https://openalex.org/W6637779999"],"related_works":["https://openalex.org/W2355927362","https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W2779562428","https://openalex.org/W4224009465","https://openalex.org/W4286629047","https://openalex.org/W4306321456","https://openalex.org/W4285260836","https://openalex.org/W3195168932","https://openalex.org/W1996541855"],"abstract_inverted_index":{"We":[0,44],"examine":[1],"the":[2,6,37,50,104,133],"ability":[3],"to":[4,14,33,48,64,73],"exploit":[5],"hierarchical":[7],"structure":[8],"of":[9,30,88,97,103],"Internet":[10,90],"addresses":[11],"in":[12],"order":[13],"endow":[15],"network":[16,35,125],"agents":[17],"with":[18,115],"predictive":[19],"capabilities.":[20],"Specifically,":[21],"we":[22],"consider":[23],"Support":[24],"Vector":[25],"Machines":[26],"(SVMs)":[27],"for":[28,106,112,117],"prediction":[29,95],"round-trip":[31],"latency":[32],"random":[34],"destinations":[36],"agent":[38],"has":[39],"not":[40],"previously":[41],"interacted":[42],"with.":[43],"use":[45],"kernel":[46],"functions":[47],"transform":[49],"structured,":[51],"yet":[52],"fragmented":[53],"and":[54,76,124],"discontinuous,":[55],"IP":[56,137],"address":[57,138],"space":[58,62],"into":[59],"a":[60,82,93],"feature":[61,128],"amenable":[63],"SVMs.":[65],"Our":[66,108],"SVM":[67,79],"approach":[68],"is":[69],"accurate,":[70],"fast,":[71],"suitable":[72],"on-line":[74],"learning":[75],"generalizes":[77],"well.":[78],"regression":[80],"on":[81],"large,":[83],"randomly":[84],"collected":[85],"data":[86],"set":[87],"30,000":[89],"latencies":[91],"yields":[92],"mean":[94],"error":[96],"25ms":[98],"using":[99],"only":[100],"20":[101],"%":[102],"samples":[105],"training.":[107],"results":[109],"are":[110],"promising":[111],"equipping":[113],"end-nodes":[114],"intelligence":[116],"service":[118],"selection,":[119],"user-directed":[120],"routing,":[121],"resource":[122],"scheduling":[123],"inference.":[126],"Finally,":[127],"selection":[129],"analysis":[130],"finds":[131],"that":[132],"eight":[134],"most":[135],"significant":[136],"bits":[139],"provide":[140],"surprisingly":[141],"strong":[142],"discriminative":[143],"power.":[144]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
