{"id":"https://openalex.org/W7124898104","doi":"https://doi.org/10.1109/cloudcom67567.2025.11331358","title":"MIN-LENS: Joint Moment Inference and Surrogate Modeling for Microservice Performance Prediction","display_name":"MIN-LENS: Joint Moment Inference and Surrogate Modeling for Microservice Performance Prediction","publication_year":2025,"publication_date":"2025-11-14","ids":{"openalex":"https://openalex.org/W7124898104","doi":"https://doi.org/10.1109/cloudcom67567.2025.11331358"},"language":null,"primary_location":{"id":"doi:10.1109/cloudcom67567.2025.11331358","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cloudcom67567.2025.11331358","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 lEEE International Conference on Cloud Computing Technology and Science (CloudCom)","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/A5042561424","display_name":"Runan Wang","orcid":"https://orcid.org/0000-0001-9245-6096"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Runan Wang","raw_affiliation_strings":["Shenzhen MSU-BIT University,Artificial Intelligence Research Institution,Guangdong,China"],"affiliations":[{"raw_affiliation_string":"Shenzhen MSU-BIT University,Artificial Intelligence Research Institution,Guangdong,China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123406757","display_name":"Wanyu Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanyu Zhang","raw_affiliation_strings":["Beijing Institute of Technology,School of Computer Science and Technology,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Computer Science and Technology,Beijing,China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5042561424"],"corresponding_institution_ids":["https://openalex.org/I4210152380"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.69544344,"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":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","score":0.9415000081062317,"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":0.9415000081062317,"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":0.03929999843239784,"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.003800000064074993,"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/inference","display_name":"Inference","score":0.6184999942779541},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.5407999753952026},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.48590001463890076},{"id":"https://openalex.org/keywords/surrogate-model","display_name":"Surrogate model","score":0.4668999910354614},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.46230000257492065},{"id":"https://openalex.org/keywords/performance-prediction","display_name":"Performance prediction","score":0.44279998540878296},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.42289999127388},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.3950999975204468},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.38499999046325684},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.37540000677108765}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7709000110626221},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6184999942779541},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.5407999753952026},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.48590001463890076},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4756999909877777},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.4668999910354614},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46380001306533813},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.46230000257492065},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.44279998540878296},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.42289999127388},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.420199990272522},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.3950999975204468},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.38499999046325684},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.37540000677108765},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.36149999499320984},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.35899999737739563},{"id":"https://openalex.org/C2781018962","wikidata":"https://www.wikidata.org/wiki/Q5164884","display_name":"Container (type theory)","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C22684755","wikidata":"https://www.wikidata.org/wiki/Q847526","display_name":"Queueing theory","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30219998955726624},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C103784038","wikidata":"https://www.wikidata.org/wiki/Q386228","display_name":"Cumulative distribution function","level":3,"score":0.29679998755455017},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.275299996137619},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.2574000060558319},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2563000023365021},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2542000114917755},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cloudcom67567.2025.11331358","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cloudcom67567.2025.11331358","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 lEEE International Conference on Cloud Computing Technology and Science (CloudCom)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8465124245","display_name":null,"funder_award_id":"62576213","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1982063824","https://openalex.org/W2011245215","https://openalex.org/W2928320209","https://openalex.org/W3040022793","https://openalex.org/W3157677190","https://openalex.org/W4308587711","https://openalex.org/W4386526919","https://openalex.org/W4387502560","https://openalex.org/W4389636146","https://openalex.org/W4393928223","https://openalex.org/W4409363526"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,145,168],"of":[2,13,69,122,187],"tail":[3],"latencies":[4],"is":[5,131,178],"critical":[6],"for":[7,27,195],"ensuring":[8],"the":[9,58,61,65,70,87,90,112,123,176,184],"performance":[10],"and":[11,83,103,114,160,201,207],"reliability":[12],"microservice-":[14],"based":[15],"cloud":[16,199],"applications.":[17],"Traditional":[18],"approaches":[19],"suffer":[20],"significant":[21],"computational":[22,185],"overhead,":[23],"making":[24],"them":[25],"unsuitable":[26],"real-time":[28],"monitoring":[29,200],"across":[30],"numerous":[31],"services.":[32],"This":[33,191],"paper":[34],"presents":[35],"MIN-LENS,":[36],"a":[37,47],"two-stage":[38],"learning":[39],"frame-work":[40],"that":[41,164],"combines":[42],"joint":[43],"moment":[44],"inference":[45],"with":[46],"latency":[48],"estimation":[49],"neural":[50],"surrogate":[51,92,130],"modeling":[52],"to":[53,107,138,140,171],"mitigate":[54],"these":[55,100],"challenges.":[56],"In":[57,86],"first":[59,66],"stage,":[60,89],"MIN":[62],"component":[63],"infers":[64],"three":[67],"moments":[68,102],"service-time":[71],"distribution":[72,126],"directly":[73,106],"from":[74],"trace-derived":[75],"features":[76],"such":[77,110],"as":[78,111,117,119],"clustered":[79],"inter-departure":[80],"times,":[81],"utilization,":[82],"arrival":[84],"variability.":[85],"second":[88],"LENS":[91],"model,":[93],"pretrained":[94],"on":[95,134,157],"high-fidelity":[96],"queueing":[97],"simulations,":[98],"maps":[99],"inferred":[101],"workload":[104],"parameters":[105],"tail-latency":[108,167],"metrics":[109],"95th":[113],"99th":[115],"percentiles,":[116],"well":[118],"selected":[120],"points":[121],"complementary":[124],"cumulative":[125],"function":[127],"(CCDF).":[128],"The":[129],"then":[132],"fine-tuned":[133],"real":[135],"microservice":[136],"traces":[137],"adapt":[139],"platform-specific":[141],"behaviors,":[142],"enabling":[143,205],"accurate":[144],"without":[146],"explicit":[147],"continuous-time":[148],"Markov":[149],"Chain":[150],"(CTMC)":[151],"solving":[152],"at":[153],"deployment":[154],"time.":[155],"Experiments":[156],"both":[158],"synthetic":[159],"real-world":[161],"datasets":[162],"demonstrate":[163],"MIN-LENS":[165,193],"reduces":[166],"error":[169],"compared":[170],"exponential":[172],"baselines":[173],"especially":[174],"when":[175],"system":[177],"under":[179],"higher":[180],"load,":[181],"while":[182],"eliminate":[183],"complexity":[186],"MAP-based":[188],"likelihood":[189],"estimation.":[190],"makes":[192],"suitable":[194],"integration":[196],"into":[197],"latency-sensitive":[198],"control":[202],"pipelines,":[203],"thus,":[204],"timely":[206],"informed":[208],"operational":[209],"decisions.":[210]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-21T00:00:00"}