{"id":"https://openalex.org/W2586835517","doi":"https://doi.org/10.1109/glocomw.2016.7849067","title":"Traffic Matrix Prediction and Estimation Based on Deep Learning for Data Center Networks","display_name":"Traffic Matrix Prediction and Estimation Based on Deep Learning for Data Center Networks","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2586835517","doi":"https://doi.org/10.1109/glocomw.2016.7849067","mag":"2586835517"},"language":"en","primary_location":{"id":"doi:10.1109/glocomw.2016.7849067","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocomw.2016.7849067","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Globecom Workshops (GC Wkshps)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/conference_contribution/Traffic_matrix_prediction_and_estimation_based_on_deep_learning_for_data_center_networks/20853274","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089177690","display_name":"Laisen Nie","orcid":"https://orcid.org/0000-0001-8813-7738"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Laisen Nie","raw_affiliation_strings":["School of Computer Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046967955","display_name":"Dingde Jiang","orcid":"https://orcid.org/0000-0003-0284-5624"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dingde Jiang","raw_affiliation_strings":["School of Computer Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101422551","display_name":"Lei Guo","orcid":"https://orcid.org/0000-0003-1559-6022"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Guo","raw_affiliation_strings":["School of Computer Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005228053","display_name":"Shui Yu","orcid":"https://orcid.org/0000-0003-4485-6743"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shui Yu","raw_affiliation_strings":["School of Information Technology, Deakin University, Burwood, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information Technology, Deakin University, Burwood, VIC, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079301418","display_name":"Houbing Song","orcid":"https://orcid.org/0000-0003-2631-9223"},"institutions":[{"id":"https://openalex.org/I12097938","display_name":"West Virginia University","ror":"https://ror.org/011vxgd24","country_code":"US","type":"education","lineage":["https://openalex.org/I12097938"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Houbing Song","raw_affiliation_strings":["Department of Electrical and Computer Engineering, West Virginia University, WV, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, West Virginia University, WV, USA","institution_ids":["https://openalex.org/I12097938"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5089177690"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":5.0421,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.95754867,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.9994000196456909,"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.9994000196456909,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.998199999332428,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.996999979019165,"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/network-traffic-simulation","display_name":"Network traffic simulation","score":0.7355225682258606},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.7313777804374695},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7168371081352234},{"id":"https://openalex.org/keywords/network-traffic-control","display_name":"Network traffic control","score":0.6326326131820679},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6156526207923889},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.5882924199104309},{"id":"https://openalex.org/keywords/floating-car-data","display_name":"Floating car data","score":0.5110222101211548},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5045174360275269},{"id":"https://openalex.org/keywords/traffic-classification","display_name":"Traffic classification","score":0.46206364035606384},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4582277238368988},{"id":"https://openalex.org/keywords/network-simulation","display_name":"Network simulation","score":0.43205496668815613},{"id":"https://openalex.org/keywords/deep-belief-network","display_name":"Deep belief network","score":0.4279329180717468},{"id":"https://openalex.org/keywords/network-management","display_name":"Network management","score":0.41402700543403625},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35535991191864014},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.2566158175468445},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14603370428085327},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.10565569996833801}],"concepts":[{"id":"https://openalex.org/C94168897","wikidata":"https://www.wikidata.org/wiki/Q574324","display_name":"Network traffic simulation","level":4,"score":0.7355225682258606},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.7313777804374695},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7168371081352234},{"id":"https://openalex.org/C201100257","wikidata":"https://www.wikidata.org/wiki/Q393287","display_name":"Network traffic control","level":3,"score":0.6326326131820679},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6156526207923889},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.5882924199104309},{"id":"https://openalex.org/C64093975","wikidata":"https://www.wikidata.org/wiki/Q356677","display_name":"Floating car data","level":3,"score":0.5110222101211548},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5045174360275269},{"id":"https://openalex.org/C169988225","wikidata":"https://www.wikidata.org/wiki/Q7832484","display_name":"Traffic classification","level":3,"score":0.46206364035606384},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4582277238368988},{"id":"https://openalex.org/C139940560","wikidata":"https://www.wikidata.org/wiki/Q290036","display_name":"Network simulation","level":2,"score":0.43205496668815613},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.4279329180717468},{"id":"https://openalex.org/C129763632","wikidata":"https://www.wikidata.org/wiki/Q1454667","display_name":"Network management","level":2,"score":0.41402700543403625},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35535991191864014},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.2566158175468445},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14603370428085327},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.10565569996833801},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/glocomw.2016.7849067","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocomw.2016.7849067","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Globecom Workshops (GC Wkshps)","raw_type":"proceedings-article"},{"id":"pmh:oai:dro.deakin.edu.au:DU:30094251","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306402457","display_name":"Deakin Research Online (Deakin University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I149704539","host_organization_name":"Deakin University","host_organization_lineage":["https://openalex.org/I149704539"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"},{"id":"pmh:oai:figshare.com:article/20853274","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Traffic_matrix_prediction_and_estimation_based_on_deep_learning_for_data_center_networks/20853274","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/20853274","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Traffic_matrix_prediction_and_estimation_based_on_deep_learning_for_data_center_networks/20853274","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W950115366","https://openalex.org/W1034630047","https://openalex.org/W1886414919","https://openalex.org/W1898210062","https://openalex.org/W1976790773","https://openalex.org/W2072128103","https://openalex.org/W2085126727","https://openalex.org/W2091547010","https://openalex.org/W2100495367","https://openalex.org/W2107655596","https://openalex.org/W2117368434","https://openalex.org/W2136922672","https://openalex.org/W2150341604","https://openalex.org/W2168595508","https://openalex.org/W2171412374","https://openalex.org/W2171815518","https://openalex.org/W2178155017","https://openalex.org/W2320948616","https://openalex.org/W2334510217","https://openalex.org/W2484742730","https://openalex.org/W4205947740","https://openalex.org/W4231109964","https://openalex.org/W6722228090"],"related_works":["https://openalex.org/W2924962435","https://openalex.org/W4379534844","https://openalex.org/W2587362999","https://openalex.org/W1550043390","https://openalex.org/W1994698836","https://openalex.org/W2035170846","https://openalex.org/W2141958076","https://openalex.org/W2745966507","https://openalex.org/W3108736915","https://openalex.org/W4233316175"],"abstract_inverted_index":{"Network":[0],"traffic":[1,22,33,61,77,89,107,119,143,150,171],"analysis":[2],"is":[3],"a":[4,10,25,97,109,116,124,129,148],"crucial":[5],"technique":[6],"for":[7,87],"systematically":[8],"operating":[9],"data":[11,48,66,110],"center":[12,49,67,111],"network.":[13],"Many":[14],"network":[15,21,32,60,76,88,106,118,127,142,149,157],"management":[16],"functions":[17],"rely":[18],"on":[19,56,123],"exact":[20],"information.":[23],"Although":[24],"great":[26],"number":[27],"of":[28,59,105,141,166],"works":[29],"to":[30,100,134],"obtain":[31],"have":[34],"been":[35],"carried":[36],"out":[37],"in":[38,47,65,74,108],"traditional":[39],"ISP":[40],"networks,":[41],"they":[42],"cannot":[43],"be":[44],"employed":[45],"effectively":[46],"networks.":[50,68],"Motivated":[51],"by":[52,159,169],"that,":[53],"we":[54,145],"focus":[55],"the":[57,75,102,137,154,164],"problem":[58],"prediction":[62,78,90,120],"and":[63,79,82,91,113,128],"estimation":[64,80,151],"We":[69,94,162],"involve":[70],"deep":[71,85,98,125,155],"learning":[72],"techniques":[73],"fields,":[81],"propose":[83,115,147],"two":[84],"architectures":[86],"estimation,":[92,144],"respectively.":[93],"first":[95],"use":[96],"architecture":[99],"explore":[101],"time-varying":[103],"property":[104,140],"network,":[112],"then":[114],"novel":[117],"approach":[121],"based":[122],"belief":[126,156],"logistic":[130],"regression":[131],"model.":[132],"Meanwhile,":[133],"deal":[135],"with":[136],"highly":[138],"ill-pose":[139],"further":[146],"method":[152],"using":[153],"trained":[158],"link":[160],"counts.":[161],"validate":[163],"effectiveness":[165],"our":[167],"methodologies":[168],"real":[170],"data.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}