{"id":"https://openalex.org/W4210314101","doi":"https://doi.org/10.1109/globecom46510.2021.9685249","title":"Adaptive Multi-slot-ahead Prediction of Network Traffic with Gaussian Process","display_name":"Adaptive Multi-slot-ahead Prediction of Network Traffic with Gaussian Process","publication_year":2021,"publication_date":"2021-12-01","ids":{"openalex":"https://openalex.org/W4210314101","doi":"https://doi.org/10.1109/globecom46510.2021.9685249"},"language":"en","primary_location":{"id":"doi:10.1109/globecom46510.2021.9685249","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom46510.2021.9685249","pdf_url":null,"source":{"id":"https://openalex.org/S4363607714","display_name":"2021 IEEE Global Communications Conference (GLOBECOM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"},"type":"article","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/A5011262125","display_name":"Yitu Wang","orcid":"https://orcid.org/0000-0003-4453-5966"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yitu Wang","raw_affiliation_strings":["NTT Corporation,NTT Network Innovation Laboratory,Yokosuka,Kanagawa,Japan,239-0847"],"affiliations":[{"raw_affiliation_string":"NTT Corporation,NTT Network Innovation Laboratory,Yokosuka,Kanagawa,Japan,239-0847","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075048116","display_name":"Takayuki Nakachi","orcid":"https://orcid.org/0000-0002-7970-454X"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]},{"id":"https://openalex.org/I32915989","display_name":"University of the Ryukyus","ror":"https://ror.org/02z1n9q24","country_code":"JP","type":"education","lineage":["https://openalex.org/I32915989"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takayuki Nakachi","raw_affiliation_strings":["NTT Corporation,NTT Network Innovation Laboratory,Yokosuka,Kanagawa,Japan,239-0847","Information Technology Center, University of the Ryukyus, Okinawa, Japan"],"affiliations":[{"raw_affiliation_string":"NTT Corporation,NTT Network Innovation Laboratory,Yokosuka,Kanagawa,Japan,239-0847","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"Information Technology Center, University of the Ryukyus, Okinawa, Japan","institution_ids":["https://openalex.org/I32915989"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081996202","display_name":"Takeru Inoue","orcid":"https://orcid.org/0000-0003-1411-8010"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takeru Inoue","raw_affiliation_strings":["NTT Corporation,NTT Network Innovation Laboratory,Yokosuka,Kanagawa,Japan,239-0847"],"affiliations":[{"raw_affiliation_string":"NTT Corporation,NTT Network Innovation Laboratory,Yokosuka,Kanagawa,Japan,239-0847","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023464420","display_name":"Toru Mano","orcid":"https://orcid.org/0000-0001-8073-3623"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toru Mano","raw_affiliation_strings":["NTT Corporation,NTT Network Innovation Laboratory,Yokosuka,Kanagawa,Japan,239-0847"],"affiliations":[{"raw_affiliation_string":"NTT Corporation,NTT Network Innovation Laboratory,Yokosuka,Kanagawa,Japan,239-0847","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5011262125"],"corresponding_institution_ids":["https://openalex.org/I2251713219"],"apc_list":null,"apc_paid":null,"fwci":0.8493,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.72740907,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9923999905586243,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.986299991607666,"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/computer-science","display_name":"Computer science","score":0.7862634062767029},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6747371554374695},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5601323246955872},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5102403163909912},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.47946226596832275},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.470353364944458},{"id":"https://openalex.org/keywords/network-traffic-simulation","display_name":"Network traffic simulation","score":0.46909841895103455},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46609464287757874},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4422709047794342},{"id":"https://openalex.org/keywords/control-reconfiguration","display_name":"Control reconfiguration","score":0.43342283368110657},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.39048856496810913},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3534892499446869},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.297554075717926},{"id":"https://openalex.org/keywords/network-traffic-control","display_name":"Network traffic control","score":0.2338601052761078},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1226048469543457}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7862634062767029},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6747371554374695},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5601323246955872},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5102403163909912},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.47946226596832275},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.470353364944458},{"id":"https://openalex.org/C94168897","wikidata":"https://www.wikidata.org/wiki/Q574324","display_name":"Network traffic simulation","level":4,"score":0.46909841895103455},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46609464287757874},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4422709047794342},{"id":"https://openalex.org/C119701452","wikidata":"https://www.wikidata.org/wiki/Q5165881","display_name":"Control reconfiguration","level":2,"score":0.43342283368110657},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.39048856496810913},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3534892499446869},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.297554075717926},{"id":"https://openalex.org/C201100257","wikidata":"https://www.wikidata.org/wiki/Q393287","display_name":"Network traffic control","level":3,"score":0.2338601052761078},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1226048469543457},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom46510.2021.9685249","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom46510.2021.9685249","pdf_url":null,"source":{"id":"https://openalex.org/S4363607714","display_name":"2021 IEEE Global Communications Conference (GLOBECOM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1566674360","https://openalex.org/W1964490787","https://openalex.org/W1972893647","https://openalex.org/W2125849100","https://openalex.org/W2145563843","https://openalex.org/W2155746806","https://openalex.org/W2170125123","https://openalex.org/W2172064003","https://openalex.org/W2735793369","https://openalex.org/W2782912001","https://openalex.org/W2783656730","https://openalex.org/W2807536558","https://openalex.org/W2808455183","https://openalex.org/W2896827527","https://openalex.org/W2897025665","https://openalex.org/W2963432161","https://openalex.org/W3000508506","https://openalex.org/W3038077692","https://openalex.org/W3045505148","https://openalex.org/W3108677582","https://openalex.org/W4211049957","https://openalex.org/W4301959443","https://openalex.org/W6679072756","https://openalex.org/W6745220581","https://openalex.org/W6752789080","https://openalex.org/W7073794093"],"related_works":["https://openalex.org/W1629725936","https://openalex.org/W2251221343","https://openalex.org/W2088093866","https://openalex.org/W2783038087","https://openalex.org/W3185411971","https://openalex.org/W2387491641","https://openalex.org/W2384408398","https://openalex.org/W2981028513","https://openalex.org/W2415931830","https://openalex.org/W3131195611"],"abstract_inverted_index":{"Multi-slot-ahead":[0],"forecasting":[1],"on":[2,35,62],"network":[3,15,58],"traffic":[4,45,59,72,104],"provides":[5],"an":[6,52],"extra":[7],"degree":[8],"of":[9,20,43,94,131,140,159,165],"freedom":[10],"to":[11,112,136],"proactively":[12],"manipulate":[13],"the":[14,30,40,44,71,79,86,102,108,113,127,141,150,157,160],"resources":[16],"when":[17,38],"immediate":[18],"reconfiguration":[19],"networks":[21],"is":[22,134],"expensive":[23],"or":[24],"infeasible.":[25],"In":[26],"return,":[27],"it":[28],"challenges":[29],"existing":[31],"data-driven":[32],"learning-based":[33],"approaches":[34],"accuracy,":[36],"especially":[37],"considering":[39],"evolving":[41,103],"property":[42],"process.":[46],"To":[47,100,117],"this":[48,95],"end,":[49],"we":[50,106,148],"establish":[51],"adaptive":[53],"learning":[54,68],"framework":[55,162],"for":[56,89],"multi-slot-ahead":[57],"prediction":[60,166],"based":[61],"Gaussian":[63],"Process":[64],"(GP).":[65],"GP":[66],"facilitates":[67],"and":[69],"comprehending":[70],"process":[73],"from":[74],"a":[75,120],"Bayesian":[76],"perspective,":[77],"where":[78],"main":[80],"characteristics":[81],"can":[82],"be":[83],"encoded":[84],"into":[85],"kernel":[87,110],"function":[88],"performance":[90],"enhancement.":[91],"The":[92],"contributions":[93],"paper":[96],"are":[97],"two-fold:":[98],"1).":[99],"track":[101],"characteristics,":[105],"approximate":[107],"optimal":[109],"adapting":[111],"current":[114],"traffic.":[115],"2).":[116],"predict":[118],"in":[119,163],"large":[121],"time":[122],"horizon":[123],"without":[124],"significantly":[125],"hurt":[126],"performance,":[128],"Linear":[129],"Model":[130],"Co-regionalization":[132],"(LMC)":[133],"utilized":[135],"better":[137],"make":[138],"use":[139],"correlation":[142],"among":[143],"subsequent":[144],"multiple":[145],"time-slots.":[146],"Finally,":[147],"demonstrate":[149],"high":[151],"tracking":[152],"capability":[153],"as":[154,156],"well":[155],"superiority":[158],"proposed":[161],"terms":[164],"accuracy":[167],"through":[168],"simulation.":[169]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
