{"id":"https://openalex.org/W4400489987","doi":"https://doi.org/10.1109/netsoft60951.2024.10588943","title":"Leveraging Large Language Models for VNF Resource Forecasting","display_name":"Leveraging Large Language Models for VNF Resource Forecasting","publication_year":2024,"publication_date":"2024-06-24","ids":{"openalex":"https://openalex.org/W4400489987","doi":"https://doi.org/10.1109/netsoft60951.2024.10588943"},"language":"en","primary_location":{"id":"doi:10.1109/netsoft60951.2024.10588943","is_oa":false,"landing_page_url":"https://doi.org/10.1109/netsoft60951.2024.10588943","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 10th International Conference on Network Softwarization (NetSoft)","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/A5037784692","display_name":"Jing Su","orcid":"https://orcid.org/0000-0002-2209-9377"},"institutions":[{"id":"https://openalex.org/I178169726","display_name":"Southern Methodist University","ror":"https://ror.org/042tdr378","country_code":"US","type":"education","lineage":["https://openalex.org/I178169726"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jing Su","raw_affiliation_strings":["Southern Methodist University,AT&T Center for Virtualization,Dallas,USA"],"affiliations":[{"raw_affiliation_string":"Southern Methodist University,AT&T Center for Virtualization,Dallas,USA","institution_ids":["https://openalex.org/I178169726"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112639076","display_name":"Suku Nair","orcid":"https://orcid.org/0000-0002-7966-2343"},"institutions":[{"id":"https://openalex.org/I178169726","display_name":"Southern Methodist University","ror":"https://ror.org/042tdr378","country_code":"US","type":"education","lineage":["https://openalex.org/I178169726"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suku Nair","raw_affiliation_strings":["Southern Methodist University,AT&T Center for Virtualization,Dallas,USA"],"affiliations":[{"raw_affiliation_string":"Southern Methodist University,AT&T Center for Virtualization,Dallas,USA","institution_ids":["https://openalex.org/I178169726"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038196096","display_name":"Leo Popokh","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122178","display_name":"Hewlett Packard Enterprise (United States)","ror":"https://ror.org/020x0c621","country_code":"US","type":"company","lineage":["https://openalex.org/I4210122178"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leo Popokh","raw_affiliation_strings":["Hewlett Packard Enterprise,Dallas,USA"],"affiliations":[{"raw_affiliation_string":"Hewlett Packard Enterprise,Dallas,USA","institution_ids":["https://openalex.org/I4210122178"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5037784692"],"corresponding_institution_ids":["https://openalex.org/I178169726"],"apc_list":null,"apc_paid":null,"fwci":2.5164,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.90519404,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"258","last_page":"262"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9628000259399414,"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"}},"topics":[{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9628000259399414,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9503999948501587,"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"}},{"id":"https://openalex.org/T13650","display_name":"Computational Physics and Python Applications","score":0.9027000069618225,"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":0.7884145975112915},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.4672505855560303},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.17528244853019714}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7884145975112915},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.4672505855560303},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.17528244853019714}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/netsoft60951.2024.10588943","is_oa":false,"landing_page_url":"https://doi.org/10.1109/netsoft60951.2024.10588943","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 10th International Conference on Network Softwarization (NetSoft)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"The":[0],"evolution":[1],"of":[2,59,83,121],"the":[3,11,54,75,98,103],"Network":[4,25],"[entbl]Function":[5],"Virtualization":[6],"(NFV)":[7],"paradigm":[8],"has":[9],"revolutionized":[10],"way":[12],"network":[13,36,146],"services":[14],"are":[15],"deployed,":[16],"managed,":[17],"and":[18,39,43,56,79],"scaled.":[19],"Within":[20],"this":[21,71],"transformative":[22],"landscape,":[23],"Virtual":[24],"Function":[26],"(VNF)":[27],"resource":[28,37,46,62,118,139,147],"prediction":[29,81],"emerges":[30],"as":[31,97],"a":[32,111,133],"cornerstone":[33],"for":[34,124,137],"optimizing":[35],"allocation":[38],"ensuring":[40],"service":[41],"reliability":[42],"efficiency.":[44],"Traditional":[45],"forecasting":[47],"methods":[48],"often":[49],"struggle":[50],"to":[51,53,101],"adapt":[52],"dynamic":[55],"non-linear":[57],"nature":[58],"changes":[60],"in":[61,65,144],"consumption":[63,119],"patterns":[64],"modern":[66],"telecommunication":[67],"networks.":[68],"We":[69],"address":[70],"challenge":[72],"by":[73],"leveraging":[74],"inherent":[76],"pattern":[77],"recognition":[78],"next-token":[80],"capabilities":[82],"Large":[84],"Language":[85],"Model":[86],"(LLM)":[87],"without":[88],"requiring":[89],"any":[90],"domain-specific":[91],"fine-tuning.":[92],"Our":[93,127],"study":[94],"utilizes":[95],"Llama2":[96],"foundation":[99],"model":[100],"evaluate":[102],"performance":[104],"against":[105],"widely":[106],"used":[107],"probability-based":[108],"models":[109],"on":[110],"public":[112],"VNF":[113,138],"dataset":[114],"that":[115,130],"encompasses":[116],"real-world":[117],"data":[120],"various":[122],"VNFs":[123],"comparative":[125],"analysis.":[126],"findings":[128],"suggest":[129],"LLM":[131],"offers":[132],"highly":[134],"effective":[135],"alternative":[136],"forecasting,":[140],"demonstrating":[141],"significant":[142],"potential":[143],"enhancing":[145],"management.":[148]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5}],"updated_date":"2026-03-14T08:43:22.919905","created_date":"2025-10-10T00:00:00"}