{"id":"https://openalex.org/W4391419102","doi":"https://doi.org/10.1109/noms59830.2024.10575040","title":"Energy-Aware VNF-FG Placement with Transformer-based Deep Reinforcement Learning","display_name":"Energy-Aware VNF-FG Placement with Transformer-based Deep Reinforcement Learning","publication_year":2024,"publication_date":"2024-05-06","ids":{"openalex":"https://openalex.org/W4391419102","doi":"https://doi.org/10.1109/noms59830.2024.10575040"},"language":"en","primary_location":{"id":"doi:10.1109/noms59830.2024.10575040","is_oa":false,"landing_page_url":"https://doi.org/10.1109/noms59830.2024.10575040","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"NOMS 2024-2024 IEEE Network Operations and Management Symposium","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hal.science/hal-04560843/document","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093835765","display_name":"Rania Sahraoui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rania Sahraoui","raw_affiliation_strings":["SAMOVAR, Institut Polytechnique de Paris,Télécom SudParis,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SAMOVAR, Institut Polytechnique de Paris,Télécom SudParis,France","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001436578","display_name":"Omar Houidi","orcid":"https://orcid.org/0000-0002-1659-0231"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Omar Houidi","raw_affiliation_strings":["SAMOVAR, Institut Polytechnique de Paris,Télécom SudParis,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SAMOVAR, Institut Polytechnique de Paris,Télécom SudParis,France","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006986560","display_name":"Fetia Bannour","orcid":"https://orcid.org/0000-0002-1511-3070"},"institutions":[{"id":"https://openalex.org/I4210097372","display_name":"\u00c9cole Nationale Sup\u00e9rieure d\u2019Informatique pour l\u2019Industrie et l\u2019Entreprise","ror":"https://ror.org/00tx98v53","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210097372"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Fetia Bannour","raw_affiliation_strings":["ENSIIE, SAMOVAR,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ENSIIE, SAMOVAR,France","institution_ids":["https://openalex.org/I4210097372"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6539,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.81697796,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11601","display_name":"Neuroscience and Neural Engineering","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11601","display_name":"Neuroscience and Neural Engineering","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9861999750137329,"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"}},{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9850000143051147,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7208480834960938},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6799256801605225},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5778101682662964},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.459736704826355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25952789187431335},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.20466288924217224},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.15391990542411804},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.13803741335868835},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11821407079696655},{"id":"https://openalex.org/keywords/composite-material","display_name":"Composite material","score":0.09395638108253479}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7208480834960938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6799256801605225},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5778101682662964},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.459736704826355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25952789187431335},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.20466288924217224},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.15391990542411804},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.13803741335868835},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11821407079696655},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.09395638108253479}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/noms59830.2024.10575040","is_oa":false,"landing_page_url":"https://doi.org/10.1109/noms59830.2024.10575040","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"NOMS 2024-2024 IEEE Network Operations and Management Symposium","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-04560843v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04560843","pdf_url":"https://hal.science/hal-04560843/document","source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2024 IEEE/IFIP Network Operations and Management Symposium (NOMS 2024), May 2024, Seoul, South Korea","raw_type":"Conference papers"},{"id":"pmh:oai:HAL:hal-04428636v1","is_oa":false,"landing_page_url":"https://hal.science/hal-04428636","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE/IFIP Network Operations and Management Symposium (NOMS)","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-04560843v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04560843","pdf_url":"https://hal.science/hal-04560843/document","source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2024 IEEE/IFIP Network Operations and Management Symposium (NOMS 2024), May 2024, Seoul, South Korea","raw_type":"Conference papers"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391419102.pdf"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W2000238032","https://openalex.org/W2038530381","https://openalex.org/W2043352445","https://openalex.org/W2049867014","https://openalex.org/W2105685892","https://openalex.org/W2210790443","https://openalex.org/W2334600287","https://openalex.org/W2591441959","https://openalex.org/W2763268973","https://openalex.org/W2781790117","https://openalex.org/W2788163338","https://openalex.org/W2914391504","https://openalex.org/W2915905517","https://openalex.org/W2949913798","https://openalex.org/W2962948535","https://openalex.org/W2963488398","https://openalex.org/W2980466635","https://openalex.org/W3006670753","https://openalex.org/W3092511853","https://openalex.org/W3101743077","https://openalex.org/W3128243799","https://openalex.org/W3176314363","https://openalex.org/W3187538359","https://openalex.org/W3207153880","https://openalex.org/W4225911914","https://openalex.org/W4297733535","https://openalex.org/W4318586145","https://openalex.org/W4319342119","https://openalex.org/W4381745023","https://openalex.org/W4385245566","https://openalex.org/W4389077346","https://openalex.org/W6692846177","https://openalex.org/W6745537798"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2920061524","https://openalex.org/W4310083477","https://openalex.org/W2328553770","https://openalex.org/W1977959518","https://openalex.org/W2038908348","https://openalex.org/W2107890255","https://openalex.org/W2106552856","https://openalex.org/W2145821588"],"abstract_inverted_index":{"Although":[0],"Network":[1],"Function":[2],"Virtualization":[3],"(NFV)":[4],"has":[5],"introduced":[6],"better":[7],"flexibility":[8],"and":[9,18,56,86,90,94,107,116,165,199,226,259],"agility":[10],"to":[11,27,45,77,135,189],"the":[12,29,47,68,71,80,83,137,148,158,162,173,177,191,195,206,219,253],"way":[13],"network":[14,21,34,73],"operators":[15],"design,":[16],"manage,":[17],"deploy":[19],"their":[20,54,58],"services,":[22],"it":[23],"is":[24,76,188],"still":[25],"challenging":[26],"find":[28],"optimal":[30,207,254],"real-time":[31],"placement":[32,141,213,224],"of":[33,49,53,70,82,109,150,161,176,185,194,215,251],"services":[35],"which":[36,156,171],"have":[37],"evolved":[38],"into":[39],"complex":[40,178],"dynamic":[41,138],"graphs":[42],"(or":[43],"VNF-FGs)":[44],"satisfy":[46],"Quality":[48],"Service":[50],"(QoS)":[51],"requirements":[52,175],"end-users":[55],"accommodate":[57],"dynamically":[59],"changing":[60],"service":[61,74,93,117,180,220],"demands.":[62],"Another":[63],"crucial":[64],"challenge":[65],"that":[66,209,238],"compounds":[67],"complexity":[69],"online":[72],"provisioning":[75],"efficiently":[78],"improve":[79,190],"utilization":[81],"limited":[84],"resources":[85],"reduce":[87],"energy":[88,260],"consumption":[89],"costs":[91],"for":[92],"infrastructure":[95],"providers":[96],"in":[97,218,230,249],"large-scale":[98,231],"networking":[99],"environments":[100],"such":[101],"as":[102],"5G":[103],"networks,":[104],"edge":[105],"computing,":[106],"Internet":[108],"Things":[110],"(IoT).":[111],"To":[112],"meet":[113],"both":[114,151],"user":[115],"provider":[118],"needs,":[119],"this":[120],"paper":[121],"proposes":[122],"a":[123,211],"novel":[124],"Transformer-based":[125],"Deep":[126],"Reinforcement":[127],"Learning":[128],"(DRL)":[129],"architecture,":[130],"called":[131],"TDRL":[132,240],"(Transformer":[133],"based-DRL),":[134],"address":[136],"energy-aware":[139],"VNF-FG":[140,179],"problem.":[142],"Our":[143,233],"intelligent":[144],"encoder-decoder":[145],"architecture":[146],"leverages":[147],"power":[149],"Graph":[152],"Attention":[153],"Networks":[154],"(GAT)":[155],"extract":[157],"important":[159],"features":[160],"physical":[163],"network,":[164],"sequence-to-sequence":[166],"(seq2seq)":[167],"models":[168],"with":[169],"Transformers":[170],"encode":[172],"ordered":[174],"graphs.":[181],"The":[182],"main":[183],"aim":[184],"these":[186],"techniques":[187],"combined":[192],"representation":[193],"current":[196],"state":[197],"environment,":[198],"help":[200],"our":[201,239],"actor-critic":[202],"DRL":[203],"agent":[204],"learn":[205],"policy":[208],"achieves":[210],"\"one-shot\"":[212],"decision":[214],"all":[216],"VNFs":[217],"graph,":[221],"thereby":[222],"improving":[223],"efficiency":[225],"resource":[227],"utilization,":[228],"especially":[229],"systems.":[232],"extensive":[234],"simulation":[235],"results":[236],"show":[237],"approach":[241],"significantly":[242],"outperforms":[243],"other":[244],"state-of-the-art":[245],"baseline":[246],"learning":[247],"algorithms":[248],"terms":[250],"achieving":[252],"balance":[255],"between":[256],"acceptance":[257],"ratio":[258],"efficiency.":[261]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}