{"id":"https://openalex.org/W2990816809","doi":null,"title":"QoS-Aware Machine Learning-based Multiple Resources Scheduling for Microservices in Cloud Environment","display_name":"QoS-Aware Machine Learning-based Multiple Resources Scheduling for Microservices in Cloud Environment","publication_year":2019,"publication_date":"2019-11-26","ids":{"openalex":"https://openalex.org/W2990816809","mag":"2990816809"},"language":"en","primary_location":{"id":"mag:2990816809","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1911.13208.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1911.13208.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100349557","display_name":"Lei Liu","orcid":"https://orcid.org/0000-0002-4646-2054","observed_orcids":["https://orcid.org/0000-0002-4646-2054"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lei Liu","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-4646-2054","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5100349557"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10101","display_name":"Cloud Computing and Resource Management","score":0.9887999892234802,"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":0.9887999892234802,"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":0.009100000374019146,"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/T14098","display_name":"Energy Efficiency in Computing","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/microservices","display_name":"microservices","score":0.7680000066757202},{"id":"https://openalex.org/keywords/quality-of-service","display_name":"quality of service","score":0.8619999885559082},{"id":"https://openalex.org/keywords/machine-learning-based-scheduling","display_name":"machine learning-based scheduling","score":0.7450000047683716},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"reinforcement learning","score":0.7319999933242798},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"cloud computing","score":0.5440000295639038},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":0.5920000076293945}],"concepts":[{"id":"https://openalex.org/C2778505942","wikidata":"https://www.wikidata.org/wiki/Q18344624","display_name":"Microservices","level":3,"score":0.8672967553138733},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8464117050170898},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.6676799058914185},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.6294757127761841},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.612989068031311},{"id":"https://openalex.org/C5119721","wikidata":"https://www.wikidata.org/wiki/Q220501","display_name":"Quality of service","level":2,"score":0.5687184929847717},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.5417315363883972},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.46252140402793884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34294408559799194},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.245753675699234},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.20955675840377808},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.1250211000442505},{"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":"mag:2990816809","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1911.13208.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:2990816809","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1911.13208.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1979866689","https://openalex.org/W1988115241","https://openalex.org/W2003904025","https://openalex.org/W2079942837","https://openalex.org/W2080360626","https://openalex.org/W2097117768","https://openalex.org/W2100787464","https://openalex.org/W2115910081","https://openalex.org/W2123001791","https://openalex.org/W2124237318","https://openalex.org/W2124275906","https://openalex.org/W2135039871","https://openalex.org/W2142875853","https://openalex.org/W2145339207","https://openalex.org/W2150139096","https://openalex.org/W2156077332","https://openalex.org/W2157189715","https://openalex.org/W2160121678","https://openalex.org/W2160609361","https://openalex.org/W2163605009","https://openalex.org/W2172014267","https://openalex.org/W2257979135","https://openalex.org/W2346051120","https://openalex.org/W2402144811","https://openalex.org/W2546571074","https://openalex.org/W2606722458","https://openalex.org/W2612089794","https://openalex.org/W2766507433","https://openalex.org/W2783444794","https://openalex.org/W2792814682","https://openalex.org/W2795035377","https://openalex.org/W2803197428","https://openalex.org/W2898643772","https://openalex.org/W2899060836","https://openalex.org/W2899377214","https://openalex.org/W2899396876","https://openalex.org/W2926143647","https://openalex.org/W2926384843","https://openalex.org/W2928288784","https://openalex.org/W2934584470","https://openalex.org/W2934889147","https://openalex.org/W2951768877","https://openalex.org/W2953038929","https://openalex.org/W2966056803","https://openalex.org/W2966185412","https://openalex.org/W2966814691"],"related_works":["https://openalex.org/W4297629750","https://openalex.org/W6979331722","https://openalex.org/W7213314315","https://openalex.org/W4366399864","https://openalex.org/W4312571209","https://openalex.org/W4390721647","https://openalex.org/W6903748780","https://openalex.org/W4283393617","https://openalex.org/W2803652029","https://openalex.org/W3118198138"],"abstract_inverted_index":{"Microservices":[0],"have":[1],"been":[2],"dominating":[3],"in":[4,65,76,100],"the":[5,22,27,33,40,51,56,62,77,84,93,125,160,170,199,209],"modern":[6],"cloud":[7],"environment.":[8],"To":[9,104,168],"improve":[10],"cost":[11],"efficiency,":[12,86],"multiple":[13],"microservices":[14,190],"are":[15],"normally":[16],"co-located":[17],"on":[18,192],"a":[19,110,133,176,181],"server.":[20],"Thus,":[21,142],"run-time":[23],"resource":[24,74,161],"scheduling":[25,34,78,95,114,139,217],"becomes":[26],"pivot":[28],"for":[29],"QoS":[30,102,201],"control.":[31],"However,":[32],"exploration":[35,85],"space":[36,140],"enlarges":[37],"rapidly":[38],"with":[39],"increasing":[41],"server":[42],"resources":[43,120],"-":[44,49],"cores,":[45],"cache,":[46],"bandwidth,":[47],"etc.":[48],"and":[50,121,127,132,163,174,212],"diversity":[52],"of":[53,172],"microservices.":[54],"Consequently,":[55],"existing":[57],"schedulers":[58],"might":[59],"not":[60,81],"meet":[61],"rapid":[63],"changes":[64],"service":[66],"demands.":[67],"Besides,":[68],"we":[69,108,179],"observe":[70],"that":[71,206],"there":[72],"exist":[73],"cliffs":[75],"space.":[79],"It":[80,118],"only":[82],"impacts":[83],"making":[87],"it":[88,156],"difficult":[89],"to":[90,92,137],"converge":[91],"optimal":[94,147],"solution,":[96],"but":[97],"also":[98],"results":[99,204],"severe":[101],"fluctuation.":[103],"overcome":[105],"these":[106],"problems,":[107],"propose":[109],"novel":[111],"machine":[112],"learning-based":[113],"mechanism":[115],"called":[116],"OSML.":[117],"uses":[119],"runtime":[122],"states":[123],"as":[124],"input":[126],"employs":[128],"two":[129],"MLP":[130],"models":[131],"reinforcement":[134],"learning":[135],"model":[136],"perform":[138],"exploration.":[141,167],"OSML":[143,173,207],"can":[144,157],"reach":[145],"an":[146],"solution":[148],"much":[149],"faster":[150],"than":[151],"traditional":[152],"approaches.":[153],"More":[154],"importantly,":[155],"automatically":[158],"detect":[159],"cliff":[162],"avoid":[164],"them":[165],"during":[166],"verify":[169],"effectiveness":[171],"obtain":[175],"well-generalized":[177],"model,":[178],"collect":[180],"dataset":[182],"containing":[183],"over":[184,195],"2-billion":[185],"samples":[186],"from":[187],"11":[188],"typical":[189],"running":[191],"real":[193],"servers":[194],"9":[196],"months.":[197],"Under":[198],"same":[200],"constraint,":[202],"experimental":[203],"show":[205],"outperforms":[208],"state-of-the-art":[210],"work,":[211],"achieves":[213],"around":[214],"5":[215],"times":[216],"speed.":[218]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2019-12-05T00:00:00"}
