{"id":"https://openalex.org/W3007523155","doi":"https://doi.org/10.1109/bigdata47090.2019.9006011","title":"Task Failure Prediction in Cloud Data Centers Using Deep Learning","display_name":"Task Failure Prediction in Cloud Data Centers Using Deep Learning","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007523155","doi":"https://doi.org/10.1109/bigdata47090.2019.9006011","mag":"3007523155"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006011","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006011","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5012051562","display_name":"Jiechao Gao","orcid":"https://orcid.org/0000-0003-0628-1416"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jiechao Gao","raw_affiliation_strings":["Computer Science Department, University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032698563","display_name":"Haoyu Wang","orcid":"https://orcid.org/0000-0002-3604-4799"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoyu Wang","raw_affiliation_strings":["Computer Science Department, University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064217355","display_name":"Haiying Shen","orcid":"https://orcid.org/0000-0002-7681-6255"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haiying Shen","raw_affiliation_strings":["Computer Science Department, University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5012051562"],"corresponding_institution_ids":["https://openalex.org/I51556381"],"apc_list":null,"apc_paid":null,"fwci":19.806,"has_fulltext":false,"cited_by_count":170,"citation_normalized_percentile":{"value":0.99558238,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1111","last_page":"1116"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9991000294685364,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9975000023841858,"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":0.8394923210144043},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.8126251697540283},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.651057243347168},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.6168287396430969},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.574170708656311},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5679681897163391},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5568236708641052},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.41341841220855713},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08676239848136902}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8394923210144043},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.8126251697540283},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.651057243347168},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.6168287396430969},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.574170708656311},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5679681897163391},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5568236708641052},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.41341841220855713},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08676239848136902},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006011","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006011","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1510496002","https://openalex.org/W1576520375","https://openalex.org/W1596717185","https://openalex.org/W1598796236","https://openalex.org/W1832917778","https://openalex.org/W1990867478","https://openalex.org/W1993980594","https://openalex.org/W2006778428","https://openalex.org/W2030826766","https://openalex.org/W2031513172","https://openalex.org/W2058972361","https://openalex.org/W2087387294","https://openalex.org/W2114209105","https://openalex.org/W2116261113","https://openalex.org/W2119381450","https://openalex.org/W2129542763","https://openalex.org/W2136848157","https://openalex.org/W2138784882","https://openalex.org/W2271655448","https://openalex.org/W2319441181","https://openalex.org/W2341029337","https://openalex.org/W2592735969","https://openalex.org/W2738387558","https://openalex.org/W2754002858","https://openalex.org/W2767094836","https://openalex.org/W2782968911","https://openalex.org/W2792432920","https://openalex.org/W2795948303","https://openalex.org/W2807779271","https://openalex.org/W2808242862","https://openalex.org/W2914488306","https://openalex.org/W4245040028","https://openalex.org/W6630529663","https://openalex.org/W6635679246","https://openalex.org/W6638786895","https://openalex.org/W6677408996","https://openalex.org/W6677844719","https://openalex.org/W6746980334"],"related_works":["https://openalex.org/W4244478748","https://openalex.org/W4223488648","https://openalex.org/W4205786897","https://openalex.org/W2134969820","https://openalex.org/W2251605416","https://openalex.org/W3150465815","https://openalex.org/W1997222214","https://openalex.org/W2070395303","https://openalex.org/W3020139090","https://openalex.org/W2100170515"],"abstract_inverted_index":{"A":[0],"large-scale":[1,20],"cloud":[2,21,55],"data":[3,22,119],"center":[4],"needs":[5],"to":[6,30,64,74,85,125,160,176],"provide":[7],"high":[8,26,83],"service":[9,67],"reliability":[10,53],"and":[11,36,44,57,92,113,120,137,163,181,202,207],"availability":[12],"with":[13,82,199],"low":[14],"failure":[15,27,105,129,148,206],"occurrence":[16,81],"probability.":[17],"However,":[18],"current":[19],"centers":[23],"still":[24],"face":[25],"rates":[28],"due":[29],"many":[31],"reasons":[32],"such":[33],"as":[34],"hardware":[35],"software":[37],"failures,":[38],"which":[39],"often":[40],"result":[41],"in":[42,142,166],"task":[43,76,102,162,205],"job":[45,78,104,164,208],"failures.":[46,69,122],"Such":[47],"failures":[48,79,165,209],"can":[49],"severely":[50],"reduce":[51],"the":[52,66,101,115,118,121,128,133,167,179],"of":[54,62,132,171],"services":[56],"also":[58],"occupy":[59],"huge":[60],"amount":[61],"resources":[63],"recover":[65],"from":[68],"Therefore,":[70],"it":[71],"is":[72,175],"important":[73],"predict":[75,177],"or":[77,103,185],"before":[80],"accuracy":[84,131,201],"avoid":[86],"unexpected":[87],"wastage.":[88],"Many":[89],"machine":[90,135],"learning":[91,94,136,139],"deep":[93,138],"based":[95,140,151],"methods":[96,198],"have":[97],"been":[98],"proposed":[99],"for":[100,204],"prediction":[106,130,149,173,197],"by":[107],"analyzing":[108],"past":[109],"system":[110],"message":[111],"logs":[112],"identifying":[114],"relationship":[116],"between":[117],"In":[123],"order":[124],"further":[126],"improve":[127],"previous":[134],"methods,":[141],"this":[143],"paper,":[144],"we":[145],"propose":[146],"a":[147],"algorithm":[150,174,193],"on":[152],"multi-layer":[153],"Bidirectional":[154],"Long":[155],"Short":[156],"Term":[157],"Memory":[158],"(Bi-LSTM)":[159],"identify":[161],"cloud.":[168],"The":[169,187],"goal":[170],"Bi-LSTM":[172],"whether":[178],"tasks":[180],"jobs":[182],"are":[183],"failed":[184],"completed.":[186],"trace-driven":[188],"experiments":[189],"show":[190],"that":[191],"our":[192],"outperforms":[194],"other":[195],"state-of-art":[196],"93%":[200],"87%":[203],"respectively.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":31},{"year":2022,"cited_by_count":27},{"year":2021,"cited_by_count":80},{"year":2020,"cited_by_count":5}],"updated_date":"2026-01-23T23:20:30.427331","created_date":"2025-10-10T00:00:00"}
