{"id":"https://openalex.org/W3205650449","doi":"https://doi.org/10.1145/3475991","title":"Horizontal Auto-Scaling for Multi-Access Edge Computing Using Safe Reinforcement Learning","display_name":"Horizontal Auto-Scaling for Multi-Access Edge Computing Using Safe Reinforcement Learning","publication_year":2021,"publication_date":"2021-10-18","ids":{"openalex":"https://openalex.org/W3205650449","doi":"https://doi.org/10.1145/3475991","mag":"3205650449"},"language":"en","primary_location":{"id":"doi:10.1145/3475991","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3475991","pdf_url":null,"source":{"id":"https://openalex.org/S136160450","display_name":"ACM Transactions on Embedded Computing Systems","issn_l":"1539-9087","issn":["1539-9087","1558-3465"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Embedded Computing Systems","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/A5063584187","display_name":"Kaustabha Ray","orcid":"https://orcid.org/0000-0003-0127-1155"},"institutions":[{"id":"https://openalex.org/I6498739","display_name":"Indian Statistical Institute","ror":"https://ror.org/00q2w1j53","country_code":"IN","type":"education","lineage":["https://openalex.org/I6498739"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Kaustabha Ray","raw_affiliation_strings":["Advanced Computing and Microelectronics Unit, Indian Statistical Institute, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"Advanced Computing and Microelectronics Unit, Indian Statistical Institute, Kolkata, India","institution_ids":["https://openalex.org/I6498739"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076542292","display_name":"Ansuman Banerjee","orcid":"https://orcid.org/0000-0003-0220-646X"},"institutions":[{"id":"https://openalex.org/I6498739","display_name":"Indian Statistical Institute","ror":"https://ror.org/00q2w1j53","country_code":"IN","type":"education","lineage":["https://openalex.org/I6498739"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ansuman Banerjee","raw_affiliation_strings":["Advanced Computing and Microelectronics Unit, Indian Statistical Institute, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"Advanced Computing and Microelectronics Unit, Indian Statistical Institute, Kolkata, India","institution_ids":["https://openalex.org/I6498739"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5063584187"],"corresponding_institution_ids":["https://openalex.org/I6498739"],"apc_list":null,"apc_paid":null,"fwci":2.2923,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.88678552,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"20","issue":"6","first_page":"1","last_page":"33"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9998999834060669,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9998999834060669,"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/T13553","display_name":"Age of Information Optimization","score":0.9966999888420105,"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":0.9957000017166138,"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/computer-science","display_name":"Computer science","score":0.8852927684783936},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8574264049530029},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.665042519569397},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.663148045539856},{"id":"https://openalex.org/keywords/server","display_name":"Server","score":0.6617534160614014},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.5969378352165222},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.5955038070678711},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5339260101318359},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.45992928743362427},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.42821332812309265},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.41488519310951233},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.3756539225578308},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3154892325401306},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24305593967437744},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.1096598207950592}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8852927684783936},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8574264049530029},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.665042519569397},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.663148045539856},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.6617534160614014},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.5969378352165222},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.5955038070678711},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5339260101318359},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.45992928743362427},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.42821332812309265},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.41488519310951233},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.3756539225578308},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3154892325401306},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24305593967437744},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.1096598207950592},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3475991","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3475991","pdf_url":null,"source":{"id":"https://openalex.org/S136160450","display_name":"ACM Transactions on Embedded Computing Systems","issn_l":"1539-9087","issn":["1539-9087","1558-3465"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Embedded Computing Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6600000262260437,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1667182463","https://openalex.org/W2078151802","https://openalex.org/W2144554277","https://openalex.org/W2195423816","https://openalex.org/W2397723600","https://openalex.org/W2416799949","https://openalex.org/W2599106590","https://openalex.org/W2729581406","https://openalex.org/W2767101511","https://openalex.org/W2782622643","https://openalex.org/W2794343007","https://openalex.org/W2801812796","https://openalex.org/W2900394813","https://openalex.org/W2903874029","https://openalex.org/W2918335825","https://openalex.org/W2918714081","https://openalex.org/W2919603306","https://openalex.org/W2930500175","https://openalex.org/W2931122162","https://openalex.org/W2944311919","https://openalex.org/W2951054802","https://openalex.org/W2963060851","https://openalex.org/W2963618158","https://openalex.org/W2966056803","https://openalex.org/W2970125901","https://openalex.org/W2970192721","https://openalex.org/W2972268941","https://openalex.org/W3009116850","https://openalex.org/W3018757597","https://openalex.org/W3019721760","https://openalex.org/W3035141897","https://openalex.org/W3035208698","https://openalex.org/W3037396281","https://openalex.org/W3040873268","https://openalex.org/W3041030307","https://openalex.org/W3042888170","https://openalex.org/W3042924535","https://openalex.org/W3048258432","https://openalex.org/W3049290327","https://openalex.org/W3090827750","https://openalex.org/W3118359622","https://openalex.org/W3124943657","https://openalex.org/W3134185639","https://openalex.org/W3183788589"],"related_works":["https://openalex.org/W3154796165","https://openalex.org/W4322761281","https://openalex.org/W4238233472","https://openalex.org/W4313463218","https://openalex.org/W187740018","https://openalex.org/W4312996489","https://openalex.org/W3111395152","https://openalex.org/W4313526662","https://openalex.org/W3106131444","https://openalex.org/W3216099748"],"abstract_inverted_index":{"Multi-Access":[0],"Edge":[1],"Computing":[2],"(MEC)":[3],"has":[4],"emerged":[5],"as":[6,125],"a":[7,73,102,126,148,183],"promising":[8],"new":[9],"paradigm":[10],"allowing":[11],"low":[12],"latency":[13,94,110,161],"access":[14],"to":[15,21,45,56,63,65,85,88,91,128,132,157,190,199,211],"services":[16,58],"deployed":[17,59],"on":[18,60,153,182],"edge":[19],"servers":[20,62],"avert":[22],"network":[23],"latencies":[24],"often":[25],"encountered":[26],"in":[27,116,179,197,203,218],"accessing":[28],"cloud":[29],"services.":[30],"A":[31],"key":[32],"component":[33],"of":[34,52,141,171,194,215],"the":[35,47,98,129,139,143,154,192,213],"MEC":[36,61,99],"environment":[37,100],"is":[38,43],"an":[39],"auto-scaling":[40,78,135],"policy":[41,79,130],"which":[42],"used":[44],"decide":[46],"overall":[48],"management":[49],"and":[50],"scaling":[51],"container":[53],"instances":[54],"corresponding":[55],"individual":[57],"cater":[64],"traffic":[66,86],"fluctuations.":[67],"In":[68],"this":[69],"work,":[70],"we":[71,206],"propose":[72],"Safe":[74],"Reinforcement":[75],"Learning":[76],"(RL)-based":[77],"agent":[80,131],"that":[81,137,165],"can":[82,112],"efficiently":[83],"adapt":[84],"variations":[87],"ensure":[89],"adherence":[90],"service":[92,159],"specific":[93,160],"requirements.":[95,162],"We":[96,107,146,163,175],"model":[97],"using":[101],"Markov":[103],"Decision":[104],"Process":[105],"(MDP).":[106],"demonstrate":[108,212],"how":[109],"requirements":[111],"be":[113],"formally":[114],"expressed":[115],"Linear":[117],"Temporal":[118],"Logic":[119],"(LTL).":[120],"The":[121],"LTL":[122,144,155],"specification":[123],"acts":[124],"guide":[127],"automatically":[133],"learn":[134],"decisions":[136],"maximize":[138],"probability":[140],"satisfying":[142],"formula.":[145],"introduce":[147],"quantitative":[149],"reward":[150,167],"mechanism":[151,168],"based":[152],"formula":[156],"tailor":[158],"prove":[164],"our":[166,195,216],"ensures":[169],"convergence":[170],"standard":[172],"Safe-RL":[173],"approaches.":[174],"present":[176],"experimental":[177],"results":[178],"practical":[180],"scenarios":[181],"test-bed":[184],"setup":[185],"with":[186],"real-world":[187],"benchmark":[188],"applications":[189],"show":[191],"effectiveness":[193,214],"approach":[196,217],"comparison":[198],"other":[200],"state-of-the-art":[201],"methods":[202],"literature.":[204],"Furthermore,":[205],"perform":[207],"extensive":[208],"simulated":[209],"experiments":[210],"large":[219],"scale":[220],"scenarios.":[221]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
