{"id":"https://openalex.org/W4394744886","doi":"https://doi.org/10.1109/tsc.2024.3387661","title":"A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functions","display_name":"A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functions","publication_year":2024,"publication_date":"2024-04-12","ids":{"openalex":"https://openalex.org/W4394744886","doi":"https://doi.org/10.1109/tsc.2024.3387661"},"language":"en","primary_location":{"id":"doi:10.1109/tsc.2024.3387661","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsc.2024.3387661","pdf_url":null,"source":{"id":"https://openalex.org/S204223317","display_name":"IEEE Transactions on Services Computing","issn_l":"1939-1374","issn":["1939-1374","2372-0204"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Services Computing","raw_type":"journal-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/A5049382049","display_name":"S. Agarwal","orcid":"https://orcid.org/0000-0003-0944-8314"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Siddharth Agarwal","raw_affiliation_strings":["Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032796449","display_name":"Maria A. Rodriguez","orcid":"https://orcid.org/0000-0002-2831-8526"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Maria A. Rodriguez","raw_affiliation_strings":["Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014716105","display_name":"Rajkumar Buyya","orcid":"https://orcid.org/0000-0001-9754-6496"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Rajkumar Buyya","raw_affiliation_strings":["Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5049382049"],"corresponding_institution_ids":["https://openalex.org/I165779595"],"apc_list":null,"apc_paid":null,"fwci":13.1496,"has_fulltext":false,"cited_by_count":40,"citation_normalized_percentile":{"value":0.99126819,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"17","issue":"5","first_page":"1899","last_page":"1910"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.779699981212616,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.779699981212616,"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/computer-science","display_name":"Computer science","score":0.8690794706344604},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8326497077941895},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.547286868095398},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4180462062358856},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.33699852228164673},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32260122895240784}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8690794706344604},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8326497077941895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.547286868095398},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4180462062358856},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.33699852228164673},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32260122895240784}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsc.2024.3387661","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsc.2024.3387661","pdf_url":null,"source":{"id":"https://openalex.org/S204223317","display_name":"IEEE Transactions on Services Computing","issn_l":"1939-1374","issn":["1939-1374","2372-0204"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Services Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1480527676","https://openalex.org/W2171916252","https://openalex.org/W2525833315","https://openalex.org/W2626970695","https://openalex.org/W2736601468","https://openalex.org/W2905507405","https://openalex.org/W2930508541","https://openalex.org/W2996705685","https://openalex.org/W3003485252","https://openalex.org/W3049730317","https://openalex.org/W3085460132","https://openalex.org/W3161207621","https://openalex.org/W3191752327","https://openalex.org/W3193057123","https://openalex.org/W3216772467","https://openalex.org/W4200608417","https://openalex.org/W4205552495","https://openalex.org/W4229038698","https://openalex.org/W4286307984","https://openalex.org/W4286906273","https://openalex.org/W4292974926","https://openalex.org/W4294002011","https://openalex.org/W4301273571","https://openalex.org/W4310009707","https://openalex.org/W4312918711","https://openalex.org/W4376279191","https://openalex.org/W4386323795","https://openalex.org/W6628627537","https://openalex.org/W6674304311","https://openalex.org/W6677939520","https://openalex.org/W6741002519","https://openalex.org/W6757188888","https://openalex.org/W6761088107","https://openalex.org/W6775201933","https://openalex.org/W6804601995","https://openalex.org/W6840371028"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3046775127","https://openalex.org/W3082895349"],"abstract_inverted_index":{"Function-as-a-Service":[0],"(FaaS)":[1],"introduces":[2],"a":[3,14,31,91,99,169,173,209,250,263],"lightweight,":[4],"function-based":[5],"cloud":[6,26,119,134],"execution":[7,275],"model":[8,168],"that":[9,102,127,224,262],"finds":[10],"its":[11],"relevance":[12],"in":[13,116,123,154,181],"range":[15],"of":[16,94,208],"applications":[17,36],"like":[18],"IoT-edge":[19],"data":[20,89],"processing":[21],"and":[22,41,53,79,121,140,167,193,228,240,260,278],"anomaly":[23],"detection.":[24],"While":[25],"service":[27],"providers":[28],"(CSPs)":[29],"offer":[30],"near-infinite":[32],"function":[33,56,191,245,258,274,283],"elasticity,":[34],"these":[35],"often":[37],"experience":[38],"fluctuating":[39],"workloads":[40],"stricter":[42],"performance":[43,100],"constraints.":[44],"A":[45,148],"typical":[46],"CSP":[47],"strategy":[48],"is":[49,158,267],"to":[50,75,113,151,159,222,235,269],"empirically":[51],"determine":[52],"adjust":[54],"desired":[55],"instances":[57],"or":[58,73,90],"resources,":[59],"known":[60],"as":[61,71,172],"<italic":[62,210],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[63,211],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">autoscaling</i>":[64],",":[65],"based":[66],"on":[67],"monitoring-based":[68],"thresholds":[69],"such":[70],"CPU":[72],"memory,":[74],"cope":[76],"with":[77,163,217,254],"demand":[78],"performance.":[80],"However,":[81],"threshold":[82],"configuration":[83],"either":[84],"requires":[85],"expert":[86],"knowledge,":[87],"historical":[88],"complete":[92],"view":[93],"the":[95,129,197,206,218,237],"environment,":[96],"making":[97,144],"autoscaling":[98,192,252,259,265],"bottleneck":[101],"lacks":[103],"an":[104,124],"adaptable":[105,125],"solution.":[106],"Reinforcement":[107],"learning":[108],"(RL)":[109],"algorithms":[110,166],"are":[111],"proven":[112],"be":[114],"beneficial":[115],"analysing":[117],"complex":[118],"environments":[120,135],"result":[122],"policy":[126],"maximizes":[128],"expected":[130],"objectives.":[131],"Most":[132],"realistic":[133],"usually":[136],"involve":[137],"operational":[138],"interference":[139],"have":[141],"limited":[142],"visibility,":[143],"them":[145,195],"partially":[146],"observable.":[147],"general":[149],"solution":[150],"tackle":[152],"observability":[153],"highly":[155],"dynamic":[156],"settings":[157],"integrate":[160],"Recurrent":[161,187],"units":[162],"model-free":[164,186,198],"RL":[165,188],"decision":[170],"process":[171],"Partially":[174],"Observable":[175],"Markov":[176],"Decision":[177],"Process":[178],"(POMDP).":[179],"Therefore,":[180],"this":[182],"paper,":[183],"we":[184],"investigate":[185],"agents":[189],"for":[190,244,280],"compare":[194,249],"against":[196],"Proximal":[199],"Policy":[200],"Optimisation":[201],"(PPO)":[202],"algorithm.":[203],"We":[204,247],"explore":[205],"integration":[207],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Long-Short":[212],"Term":[213],"Memory</i>":[214],"(LSTM)":[215],"network":[216],"state-of-the-art":[219],"PPO":[220],"algorithm":[221],"find":[223],"under":[225],"our":[226],"experimental":[227],"evaluation":[229],"settings,":[230],"recurrent":[231],"policies":[232],"were":[233],"able":[234,268],"capture":[236],"environment":[238],"parameters":[239],"show":[241],"promising":[242],"results":[243],"autoscaling.":[246],"further":[248],"PPO-based":[251],"agent":[253,266],"commercially":[255],"used":[256],"threshold-based":[257],"posit":[261],"LSTM-based":[264],"improve":[270],"throughput":[271],"by":[272,276],"18%,":[273],"13%":[277],"account":[279],"8.4%":[281],"more":[282],"instances.":[284]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":29},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2024-04-13T00:00:00"}
