{"id":"https://openalex.org/W7154994167","doi":"https://doi.org/10.1109/southeastcon63549.2026.11476043","title":"Use of Machine Learning in IaC State and Concurrency Management","display_name":"Use of Machine Learning in IaC State and Concurrency Management","publication_year":2026,"publication_date":"2026-02-20","ids":{"openalex":"https://openalex.org/W7154994167","doi":"https://doi.org/10.1109/southeastcon63549.2026.11476043"},"language":"en","primary_location":{"id":"doi:10.1109/southeastcon63549.2026.11476043","doi":"https://doi.org/10.1109/southeastcon63549.2026.11476043","is_oa":false,"landing_page_url":"https://doi.org/10.1109/southeastcon63549.2026.11476043","pdf_url":null,"source":{"id":"https://openalex.org/S4393917016","display_name":"IEEE SoutheastCon","issn_l":"1091-0050","issn":["1091-0050"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"SoutheastCon 2026","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5118955330","display_name":"Vaibhav Kumar Pandey","orcid":"https://orcid.org/0009-0001-6056-6423","observed_orcids":["https://orcid.org/0009-0001-6056-6423","https://orcid.org/0009-0008-6102-1186"]},"institutions":[{"id":"https://openalex.org/I4210127325","display_name":"Broadcom (United States)","ror":"https://ror.org/035gt5s03","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127325"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vaibhav Pandey","raw_affiliation_strings":["Broadcom Inc,Software Engineering,Atlanta,USA"],"raw_orcid":"https://orcid.org/0009-0001-6056-6423","affiliations":[{"raw_affiliation_string":"Broadcom Inc,Software Engineering,Atlanta,USA","institution_ids":["https://openalex.org/I4210127325"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5118955330"],"corresponding_institution_ids":["https://openalex.org/I4210127325"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T12127","display_name":"Software System Performance and Reliability","score":0.8080999851226807,"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/T12127","display_name":"Software System Performance and Reliability","score":0.8080999851226807,"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.12929999828338623,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10260","display_name":"Software Engineering Research","score":0.025299999862909317,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/infrastructure-as-code","display_name":"infrastructure as code","score":0.9819999933242798},{"id":"https://openalex.org/keywords/concurrency-control","display_name":"concurrency control","score":0.9169999957084656},{"id":"https://openalex.org/keywords/state-consistency","display_name":"state consistency","score":0.8840000033378601},{"id":"https://openalex.org/keywords/conflict-detection","display_name":"conflict detection","score":0.621999979019165},{"id":"https://openalex.org/keywords/cloud-infrastructure-management","display_name":"cloud infrastructure management","score":0.5979999899864197},{"id":"https://openalex.org/keywords/terraform","display_name":"Terraform","score":0.746999979019165},{"id":"https://openalex.org/keywords/ansible","display_name":"Ansible","score":0.640999972820282},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.5099999904632568},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.7350000143051147},{"id":"https://openalex.org/keywords/explainability","display_name":"explainability","score":0.621999979019165}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8115000128746033},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5092999935150146},{"id":"https://openalex.org/C193702766","wikidata":"https://www.wikidata.org/wiki/Q1414548","display_name":"Concurrency","level":2,"score":0.5041999816894531},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4894999861717224},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44279998540878296},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.3481999933719635},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C50292564","wikidata":"https://www.wikidata.org/wiki/Q2462783","display_name":"Computational learning theory","level":3,"score":0.31709998846054077},{"id":"https://openalex.org/C2775941552","wikidata":"https://www.wikidata.org/wiki/Q25212305","display_name":"Isolation (microbiology)","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C167822520","wikidata":"https://www.wikidata.org/wiki/Q176452","display_name":"Finite-state machine","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C84511453","wikidata":"https://www.wikidata.org/wiki/Q2914952","display_name":"Concurrency control","level":3,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/southeastcon63549.2026.11476043","doi":"https://doi.org/10.1109/southeastcon63549.2026.11476043","is_oa":false,"landing_page_url":"https://doi.org/10.1109/southeastcon63549.2026.11476043","pdf_url":null,"source":{"id":"https://openalex.org/S4393917016","display_name":"IEEE SoutheastCon","issn_l":"1091-0050","issn":["1091-0050"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"SoutheastCon 2026","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":1,"referenced_works":["https://openalex.org/W3157441214"],"related_works":["https://openalex.org/W7150711605","https://openalex.org/W4414970489","https://openalex.org/W4416618457","https://openalex.org/W4411152632","https://openalex.org/W7114999841","https://openalex.org/W7212048812","https://openalex.org/W7215032159","https://openalex.org/W7131064058","https://openalex.org/W2883644649","https://openalex.org/W7205268386"],"abstract_inverted_index":{"Infrastructure":[0],"as":[1,6,29],"Code":[2],"(IaC)":[3],"has":[4],"emerged":[5],"a":[7,22,81,173],"cornerstone":[8],"of":[9,57,84,132],"modern":[10],"cloud-native":[11],"application":[12],"management,":[13],"yet":[14],"ensuring":[15],"state":[16,41],"consistency":[17],"and":[18,31,49,74,88,94,135,153,167,181],"concurrency":[19,64,141,179],"safety":[20],"remains":[21],"persistent":[23],"challenge.":[24],"Conventional":[25],"IaC":[26,63,178],"tools":[27],"such":[28],"Terraform":[30],"Ansible":[32],"rely":[33],"on":[34],"coarse-grained":[35],"locking":[36,77],"mechanisms":[37],"that":[38,107],"prevent":[39],"concurrent":[40],"updates":[42,134],"but":[43],"often":[44],"introduce":[45],"bottlenecks,":[46],"idle":[47],"waiting,":[48],"reduced":[50],"parallelism.":[51],"This":[52],"work":[53,171],"explores":[54],"the":[55,114,164],"use":[56],"Machine":[58],"Learning":[59],"(ML)":[60],"to":[61,128],"enhance":[62],"control":[65],"by":[66],"predicting":[67],"potential":[68],"conflicts,":[69],"estimating":[70],"optimal":[71],"lock":[72],"durations,":[73],"dynamically":[75],"adjusting":[76],"granularity.":[78],"We":[79,145],"generated":[80],"large-scale":[82],"dataset":[83],"124,000":[85],"state-modification":[86],"events":[87],"evaluated":[89],"both":[90],"Random":[91],"Forest":[92],"(RF)":[93],"Long":[95],"Short-Term":[96],"Memory":[97],"(LSTM)":[98],"models":[99],"using":[100],"stratified":[101],"group":[102],"k-fold":[103],"cross-validation.":[104],"Results":[105],"show":[106],"RF":[108],"achieves":[109],"strong":[110],"baseline":[111],"performance":[112],"while":[113],"LSTM":[115],"consistently":[116],"outperforms":[117],"tree-based":[118],"baselines":[119],"in":[120,150],"capturing":[121],"sequential":[122],"conflict":[123],"patterns.":[124],"These":[125],"improvements":[126],"translate":[127],"more":[129],"accurate":[130],"detection":[131],"high-risk":[133],"lower":[136],"false":[137],"positives,":[138],"enabling":[139],"finer-grained":[140],"without":[142],"sacrificing":[143],"safety.":[144],"further":[146],"analyze":[147],"model":[148],"overhead":[149],"deployment":[151],"contexts":[152],"highlight":[154],"design":[155],"trade-offs":[156],"for":[157,176,185],"explainability":[158],"versus":[159],"predictive":[160],"accuracy.":[161],"By":[162],"releasing":[163],"dataset,":[165],"models,":[166],"training":[168],"pipeline,":[169],"this":[170],"provides":[172],"reproducible":[174],"foundation":[175],"ML-driven":[177],"management":[180],"opens":[182],"new":[183],"directions":[184],"adaptive,":[186],"conflict-aware":[187],"DevOps":[188],"automation.":[189]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-04-21T00:00:00"}
