{"id":"https://openalex.org/W7162338340","doi":"https://doi.org/10.1109/access.2026.3696723","title":"Machine Learning for Network Optimization: An Empirical Performance Analysis Across Diverse Traffic Scenarios","display_name":"Machine Learning for Network Optimization: An Empirical Performance Analysis Across Diverse Traffic Scenarios","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7162338340","doi":"https://doi.org/10.1109/access.2026.3696723"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3696723","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3696723","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3696723","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136944670","display_name":"Ansa Niaz","orcid":null},"institutions":[{"id":"https://openalex.org/I1323252656","display_name":"Information Technology University","ror":"https://ror.org/00ngv8j44","country_code":"PK","type":"education","lineage":["https://openalex.org/I1323252656"]},{"id":"https://openalex.org/I172780181","display_name":"University of the Punjab","ror":"https://ror.org/011maz450","country_code":"PK","type":"education","lineage":["https://openalex.org/I172780181"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Ansa Niaz","raw_affiliation_strings":["Department of Electrical Engineering, Information Technology University, Lahore, Punjab, Pakistan"],"raw_orcid":"https://orcid.org/0009-0001-7228-2305","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Information Technology University, Lahore, Punjab, Pakistan","institution_ids":["https://openalex.org/I172780181","https://openalex.org/I1323252656"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124877931","display_name":"Muhammad Saqib Niaz","orcid":null},"institutions":[{"id":"https://openalex.org/I78978612","display_name":"Yangzhou University","ror":"https://ror.org/03tqb8s11","country_code":"CN","type":"education","lineage":["https://openalex.org/I78978612"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Muhammad Saqib Niaz","raw_affiliation_strings":["College of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou, Jiangsu, China"],"raw_orcid":"https://orcid.org/0000-0002-0545-6539","affiliations":[{"raw_affiliation_string":"College of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou, Jiangsu, China","institution_ids":["https://openalex.org/I78978612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136972096","display_name":"Kazim Raza Talpur","orcid":null},"institutions":[{"id":"https://openalex.org/I4528857","display_name":"University of Kuala Lumpur","ror":"https://ror.org/026wwrx19","country_code":"MY","type":"education","lineage":["https://openalex.org/I4528857"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Kazim Raza Talpur","raw_affiliation_strings":["Department of Intelligence Informatics, Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia"],"raw_orcid":"https://orcid.org/0000-0002-6135-0165","affiliations":[{"raw_affiliation_string":"Department of Intelligence Informatics, Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia","institution_ids":["https://openalex.org/I4528857"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036697759","display_name":"Bandeh Ali Talpur","orcid":"https://orcid.org/0000-0002-5047-9254"},"institutions":[{"id":"https://openalex.org/I205274468","display_name":"Trinity College Dublin","ror":"https://ror.org/02tyrky19","country_code":"IE","type":"education","lineage":["https://openalex.org/I205274468"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Bandeh Ali Talpur","raw_affiliation_strings":["School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, Ireland"],"raw_orcid":"https://orcid.org/0000-0002-5047-9254","affiliations":[{"raw_affiliation_string":"School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, Ireland","institution_ids":["https://openalex.org/I205274468"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.82018693,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"85478","last_page":"85494"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10714","display_name":"Software-Defined Networks and 5G","score":0.5217000246047974,"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":0.5217000246047974,"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/T10138","display_name":"Network Traffic and Congestion Control","score":0.07909999787807465,"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/T10847","display_name":"Advanced Optical Network Technologies","score":0.06949999928474426,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3828999996185303},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.3725999891757965},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3659999966621399},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.3010999858379364},{"id":"https://openalex.org/keywords/computational-learning-theory","display_name":"Computational learning theory","score":0.29190000891685486},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.288100004196167}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7975000143051147},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6055999994277954},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6015999913215637},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3659999966621399},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3010999858379364},{"id":"https://openalex.org/C50292564","wikidata":"https://www.wikidata.org/wiki/Q2462783","display_name":"Computational learning theory","level":3,"score":0.29190000891685486},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C2987015589","wikidata":"https://www.wikidata.org/wiki/Q1040098","display_name":"Learning network","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.272599995136261},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2635999917984009}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3696723","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3696723","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a97cff4fbedc430ea89756b37a0938f6","is_oa":true,"landing_page_url":"https://doaj.org/article/a97cff4fbedc430ea89756b37a0938f6","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 85478-85494 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3696723","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3696723","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,58,148,200],"objective":[1],"of":[2,216],"this":[3,32],"scientific":[4],"study":[5,59],"is":[6,35,126,203,231,235,240,245],"to":[7,24,112,160,196],"determine":[8],"under":[9,143,168],"which":[10],"traffic":[11,105,146,234,249],"conditions":[12],"machinelearning-based":[13],"routing":[14,23,34],"control":[15],"provides":[16],"sufficient":[17],"benefit":[18],"over":[19],"conventional":[20,64,162],"equal-cost":[21,65,197],"multi-path":[22,66,198],"justify":[25],"its":[26],"additional":[27,103],"operational":[28,238],"complexity.":[29],"To":[30],"address":[31],"objective,":[33],"formulated":[36],"as":[37],"a":[38,45,54,63,68,74,79,86,108],"sequential":[39],"decision-making":[40],"problem":[41],"and":[42,78,98,123,130,140,157,170,190,209,237,252],"evaluated":[43],"through":[44],"controlled":[46],"empirical":[47],"comparison":[48],"in":[49,206],"Network":[50],"Simulator":[51],"3":[52],"using":[53,128],"fat-tree":[55],"data-center":[56],"topology.":[57],"compares":[60],"three":[61],"controllers:":[62],"baseline,":[67,163],"supervised":[69,201,229],"learning":[70,82,181,230,244],"controller":[71,83,182,202,219],"based":[72,84],"on":[73,85],"temporal":[75],"convolutional":[76],"network,":[77],"deep":[80,87,179],"reinforcement":[81,180,218,243],"Q-network.":[88],"Traffic":[89],"workloads":[90],"are":[91],"derived":[92],"from":[93,107],"public":[94,109],"Internet":[95],"backbone,":[96],"long-haul,":[97],"campus-network":[99],"trace":[100],"repositories,":[101],"with":[102,164],"distributed-denial-of-service":[104],"injected":[106],"attack":[110],"dataset":[111],"construct":[113],"four":[114],"operating":[115],"regimes:":[116],"Core":[117],"Internet,":[118],"Long-Haul":[119],"Diurnal,":[120],"Enterprise":[121],"Campus,":[122],"Stress/Attack.":[124],"Performance":[125],"assessed":[127],"average":[129,184],"99th-percentile":[131],"flow":[132,185],"completion":[133,186],"time,":[134],"receiver":[135],"goodput,":[136],"packet":[137,191],"loss":[138,192],"rate,":[139],"inference":[141,214],"latency":[142,156,215],"heterogeneous":[144],"trace-driven":[145],"conditions.":[147],"results":[149],"show":[150],"that":[151,228],"both":[152],"learning-based":[153],"controllers":[154],"improve":[155],"reliability":[158],"relative":[159,195],"the":[161,165,174,178,213,217],"largest":[166],"gains":[167],"volatile":[169],"adversarial":[171],"traffic.":[172],"In":[173],"aggregate":[175],"medium-load":[176],"evaluation,":[177],"reduces":[183],"time":[187],"by":[188,193],"33.4%":[189],"80.7%":[194],"routing.":[199],"more":[204,258],"competitive":[205],"stable":[207],"regimes":[208],"delivers":[210],"about":[211],"half":[212],"(0.8":[220],"ms":[221],"versus":[222],"1.6":[223],"ms).":[224],"These":[225],"findings":[226],"indicate":[227],"attractive":[232],"when":[233,248],"predictable":[236],"simplicity":[239],"important,":[241],"whereas":[242],"better":[246],"justified":[247],"non-stationarity,":[250],"burstiness,":[251],"attack-driven":[253],"stress":[254],"make":[255],"long-horizon":[256],"adaptation":[257],"valuable.":[259]},"counts_by_year":[],"updated_date":"2026-06-14T07:44:22.658603","created_date":"2026-05-26T00:00:00"}