{"id":"https://openalex.org/W7147275041","doi":"https://doi.org/10.1109/cnml68938.2026.11453230","title":"ArcheScale-Guard: Archetype-Aware Predictive Autoscaling with Uncertainty Quantification for Serverless Computing","display_name":"ArcheScale-Guard: Archetype-Aware Predictive Autoscaling with Uncertainty Quantification for Serverless Computing","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147275041","doi":"https://doi.org/10.1109/cnml68938.2026.11453230"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11453230","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11453230","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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":null,"display_name":"Ao Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ao Zhu","raw_affiliation_strings":["University of Pennsylvania,Philadelphia,USA"],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Weicheng Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weicheng Liu","raw_affiliation_strings":["University of Texas at Dallas,Richardson,USA"],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhongkang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhongkang Li","raw_affiliation_strings":["New York University,New York,USA"],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhaocheng Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhaocheng Liu","raw_affiliation_strings":["Northeastern University,Boston,USA"],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Jiarong Qiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiarong Qiu","raw_affiliation_strings":["University of Southern California,Los Angeles,USA"],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Chenfeiyu Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chenfeiyu Wen","raw_affiliation_strings":["New York University,New York,USA"],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.97391566,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1425","last_page":"1429"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10101","display_name":"Cloud Computing and Resource Management","score":0.6345999836921692,"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"}},"topics":[{"id":"https://openalex.org/T10101","display_name":"Cloud Computing and Resource Management","score":0.6345999836921692,"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/T12127","display_name":"Software System Performance and Reliability","score":0.25600001215934753,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.03709999844431877,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.5874000191688538},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5688999891281128},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.5185999870300293},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.45559999346733093},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.37229999899864197},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.33320000767707825}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7264999747276306},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.5874000191688538},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5688999891281128},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5568000078201294},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.5185999870300293},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.37229999899864197},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3294000029563904},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3260999917984009},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30889999866485596},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3010999858379364},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11453230","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11453230","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6927116513252258,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2560531208","https://openalex.org/W3120636638","https://openalex.org/W4308621130","https://openalex.org/W4386768622","https://openalex.org/W4389670351","https://openalex.org/W4396575060","https://openalex.org/W4401670203","https://openalex.org/W4403489759","https://openalex.org/W4407937593","https://openalex.org/W4416922888","https://openalex.org/W4417471200","https://openalex.org/W7116694432","https://openalex.org/W7117455493","https://openalex.org/W7124449160","https://openalex.org/W7124724110","https://openalex.org/W7131064415","https://openalex.org/W7138457430","https://openalex.org/W7140192390","https://openalex.org/W7140224197","https://openalex.org/W7140225026","https://openalex.org/W7147116554"],"related_works":[],"abstract_inverted_index":{"Serverless":[0],"computing":[1],"platforms":[2],"face":[3],"significant":[4],"challenges":[5],"in":[6],"managing":[7],"cold":[8,105],"start":[9,106],"latency":[10],"while":[11,24,120],"optimizing":[12],"resource":[13,123,139],"costs.":[14],"Traditional":[15],"reactive":[16,118],"autoscaling":[17,42],"policies":[18],"fail":[19],"to":[20,66,111,117,131],"anticipate":[21],"workload":[22,46,69],"fluctuations,":[23],"single-model":[25],"predictive":[26,41],"approaches":[27],"struggle":[28],"with":[29,49,85],"the":[30,54,75,95],"heterogeneous":[31],"nature":[32],"of":[33],"real-world":[34],"workloads.":[35],"We":[36],"present":[37],"ArcheScale-Guard,":[38],"a":[39],"two-stage":[40],"framework":[43],"that":[44,102],"combines":[45],"archetype":[47],"classification":[48],"uncertainty-aware":[50],"demand":[51],"forecasting.":[52],"In":[53,74],"first":[55],"stage,":[56,77],"we":[57],"employ":[58],"Dynamic":[59],"Time":[60],"Warping":[61],"(DTW)":[62],"based":[63],"k-medoids":[64],"clustering":[65],"identify":[67],"distinct":[68],"archetypes":[70],"(bursty,":[71],"periodic,":[72],"gradual).":[73],"second":[76],"archetype-specific":[78],"quantile":[79,143],"regression":[80],"models":[81],"provide":[82],"probabilistic":[83],"predictions":[84],"prediction":[86],"intervals,":[87],"enabling":[88],"risk-constrained":[89],"scaling":[90],"decisions.":[91],"Experimental":[92],"evaluation":[93],"on":[94],"Azure":[96],"Functions":[97],"Trace":[98],"2019":[99],"dataset":[100],"demonstrates":[101],"ArcheScale-Guard":[103],"reduces":[104],"rates":[107],"by":[108],"38.7%":[109],"compared":[110,116],"prediction-only":[112],"baselines":[113],"and":[114,138],"61.7%":[115],"policies,":[119],"maintaining":[121],"competitive":[122],"efficiency.":[124],"The":[125],"uncertainty":[126],"quantification":[127],"mechanism":[128],"allows":[129],"operators":[130],"explicitly":[132],"trade":[133],"off":[134],"between":[135],"SLA":[136],"violations":[137],"costs":[140],"through":[141],"configurable":[142],"thresholds.":[144]},"counts_by_year":[],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2026-03-10T00:00:00"}
