{"id":"https://openalex.org/W4402661237","doi":"https://doi.org/10.1109/tcc.2024.3461649","title":"WorkloadDiff: Conditional Denoising Diffusion Probabilistic Models for Cloud Workload Prediction","display_name":"WorkloadDiff: Conditional Denoising Diffusion Probabilistic Models for Cloud Workload Prediction","publication_year":2024,"publication_date":"2024-09-16","ids":{"openalex":"https://openalex.org/W4402661237","doi":"https://doi.org/10.1109/tcc.2024.3461649"},"language":"en","primary_location":{"id":"doi:10.1109/tcc.2024.3461649","doi":"https://doi.org/10.1109/tcc.2024.3461649","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcc.2024.3461649","pdf_url":null,"source":{"id":"https://openalex.org/S2492498579","display_name":"IEEE Transactions on Cloud Computing","issn_l":"2168-7161","issn":["2168-7161","2372-0018"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["ccf-b","cwts-core","dongbi-b","jufo-1","norway-1","poland-200","russia-white-list-1"],"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 Cloud Computing","raw_type":"journal-article"},"volume":null,"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/A5101630982","display_name":"Weiping Zheng","orcid":"https://orcid.org/0009-0006-8593-0558","observed_orcids":["https://orcid.org/0009-0006-8593-0558"]},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiping Zheng","raw_affiliation_strings":["School of Computer Science, South China Normal University, Guangzhou, China","School of Computer Science, South China Normal University, No.55 West of Zhongshan Avenue, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0006-8593-0558","affiliations":[{"raw_affiliation_string":"School of Computer Science, South China Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]},{"raw_affiliation_string":"School of Computer Science, South China Normal University, No.55 West of Zhongshan Avenue, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005585108","display_name":"Zongxiao Chen","orcid":"https://orcid.org/0009-0001-3483-7699","observed_orcids":["https://orcid.org/0009-0001-3483-7699"]},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongxiao Chen","raw_affiliation_strings":["School of Computer Science, South China Normal University, Guangzhou, China","School of Computer Science, South China Normal University, No.55 West of Zhongshan Avenue, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0001-3483-7699","affiliations":[{"raw_affiliation_string":"School of Computer Science, South China Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]},{"raw_affiliation_string":"School of Computer Science, South China Normal University, No.55 West of Zhongshan Avenue, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Kaiyuan Zheng","orcid":"https://orcid.org/0009-0006-2780-0616","observed_orcids":["https://orcid.org/0009-0006-2780-0616"]},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaiyuan Zheng","raw_affiliation_strings":["School of Computer Science, South China Normal University, Guangzhou, China","School of Computer Science, South China Normal University, No.55 West of Zhongshan Avenue, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0006-2780-0616","affiliations":[{"raw_affiliation_string":"School of Computer Science, South China Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]},{"raw_affiliation_string":"School of Computer Science, South China Normal University, No.55 West of Zhongshan Avenue, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113391496","display_name":"Weijian Zheng","orcid":"https://orcid.org/0009-0001-5407-6583","observed_orcids":["https://orcid.org/0009-0001-5407-6583"]},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weijian Zheng","raw_affiliation_strings":["School of Computer Science, South China Normal University, Guangzhou, China","School of Computer Science, South China Normal University, No.55 West of Zhongshan Avenue, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0001-5407-6583","affiliations":[{"raw_affiliation_string":"School of Computer Science, South China Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]},{"raw_affiliation_string":"School of Computer Science, South China Normal University, No.55 West of Zhongshan Avenue, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yiqi Chen","orcid":"https://orcid.org/0009-0001-4547-0626","observed_orcids":["https://orcid.org/0009-0001-4547-0626"]},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiqi Chen","raw_affiliation_strings":["School of Computer Science, South China Normal University, Guangzhou, China","School of Computer Science, South China Normal University, No.55 West of Zhongshan Avenue, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0001-4547-0626","affiliations":[{"raw_affiliation_string":"School of Computer Science, South China Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]},{"raw_affiliation_string":"School of Computer Science, South China Normal University, No.55 West of Zhongshan Avenue, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044447422","display_name":"Xiaomao Fan","orcid":"https://orcid.org/0000-0001-8160-1294","observed_orcids":["https://orcid.org/0000-0001-8160-1294"]},"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":false,"raw_author_name":"Xiaomao Fan","raw_affiliation_strings":["College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-8160-1294","affiliations":[{"raw_affiliation_string":"College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2495,"currency":"USD","value_usd":2495},"apc_paid":null,"fwci":1.5189,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.88069782,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"12","issue":"4","first_page":"1291","last_page":"1304"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10101","display_name":"Cloud Computing and Resource Management","score":0.9980000257492065,"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":0.9980000257492065,"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/T14098","display_name":"Energy Efficiency in Computing","score":0.0007999999797903001,"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"}},{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","score":0.00019999999494757503,"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/cloud-workload-prediction","display_name":"cloud workload prediction","score":0.8460000157356262},{"id":"https://openalex.org/keywords/denoising-diffusion-probabilistic-models","display_name":"denoising diffusion probabilistic models","score":0.9829999804496765},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"probabilistic forecasting","score":0.8790000081062317},{"id":"https://openalex.org/keywords/multivariate-time-series","display_name":"multivariate time series","score":0.7990000247955322},{"id":"https://openalex.org/keywords/multiscale-feature-extraction","display_name":"multi-scale feature extraction","score":0.9909999966621399},{"id":"https://openalex.org/keywords/adaptive-feature-fusion","display_name":"adaptive feature fusion","score":0.9309999942779541},{"id":"https://openalex.org/keywords/cloud-resource-management","display_name":"cloud resource management","score":0.7400000095367432},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"energy consumption","score":0.5839999914169312}],"concepts":[{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.7798597812652588},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7311407327651978},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7089484930038452},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.5460649728775024},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.4917655289173126},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34788405895233154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32877522706985474},{"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/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcc.2024.3461649","doi":"https://doi.org/10.1109/tcc.2024.3461649","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcc.2024.3461649","pdf_url":null,"source":{"id":"https://openalex.org/S2492498579","display_name":"IEEE Transactions on Cloud Computing","issn_l":"2168-7161","issn":["2168-7161","2372-0018"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["ccf-b","cwts-core","dongbi-b","jufo-1","norway-1","poland-200","russia-white-list-1"],"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 Cloud Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G6910369604","display_name":"\u57fa\u4e8e\u9065\u611f\u5f71\u50cf\u65f6\u7a7a\u8c31\u7279\u5f81\u8054\u5408\u7684\u57ce\u5e02\u66f4\u65b0\u77e5\u8bc6\u56fe\u8c31\u6784\u5efa\u7814\u7a76","funder_award_id":"42071399","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":47,"referenced_works":["https://openalex.org/W1959608418","https://openalex.org/W1984255960","https://openalex.org/W2064675550","https://openalex.org/W2067688816","https://openalex.org/W2109606373","https://openalex.org/W2143039774","https://openalex.org/W2286929393","https://openalex.org/W2461411193","https://openalex.org/W2519091744","https://openalex.org/W2559444735","https://openalex.org/W2604847698","https://openalex.org/W2607045400","https://openalex.org/W2608354815","https://openalex.org/W2891974069","https://openalex.org/W2901895730","https://openalex.org/W2946709941","https://openalex.org/W2953169926","https://openalex.org/W2963149401","https://openalex.org/W2965302736","https://openalex.org/W2981973403","https://openalex.org/W2986615528","https://openalex.org/W2991089415","https://openalex.org/W2992296379","https://openalex.org/W2999081893","https://openalex.org/W3036167779","https://openalex.org/W3087665158","https://openalex.org/W3094897602","https://openalex.org/W3121702752","https://openalex.org/W3122315731","https://openalex.org/W3137802491","https://openalex.org/W3181975995","https://openalex.org/W3206408938","https://openalex.org/W3209366914","https://openalex.org/W4221005058","https://openalex.org/W4226321747","https://openalex.org/W4283809531","https://openalex.org/W4292753694","https://openalex.org/W4293246087","https://openalex.org/W4294564724","https://openalex.org/W4310693685","https://openalex.org/W4312497550","https://openalex.org/W4313229743","https://openalex.org/W4318586089","https://openalex.org/W4360884927","https://openalex.org/W4385567802","https://openalex.org/W4387195417","https://openalex.org/W7133194541"],"related_works":["https://openalex.org/W2000785801","https://openalex.org/W986318368","https://openalex.org/W2384410913","https://openalex.org/W2352878646","https://openalex.org/W2004734601","https://openalex.org/W2130149817","https://openalex.org/W2990194547","https://openalex.org/W1480123525","https://openalex.org/W2620865396","https://openalex.org/W2547038763"],"abstract_inverted_index":{"Accurate":[0],"workload":[1,70],"forecasting":[2],"plays":[3],"a":[4,57,83,90,118,146],"crucial":[5],"role":[6],"in":[7,17,30,152],"optimizing":[8],"resource":[9,150],"allocation,":[10],"enhancing":[11],"performance,":[12],"and":[13,76,95,113],"reducing":[14],"energy":[15],"consumption":[16],"cloud":[18,153],"data":[19,154],"centers.":[20,155],"Deep":[21],"learning-based":[22],"methods":[23,39],"have":[24],"emerged":[25],"as":[26,145],"the":[27,41,106,122,134],"dominant":[28],"approach":[29,99],"this":[31,53],"field,":[32],"exhibiting":[33],"exceptional":[34],"performance.":[35],"However,":[36],"most":[37],"existing":[38],"lack":[40],"ability":[42],"to":[43,100],"quantify":[44],"confidence,":[45],"limiting":[46],"their":[47],"practical":[48],"decision-making":[49],"utility.":[50],"To":[51,108],"address":[52],"limitation,":[54],"we":[55,88,116],"propose":[56],"novel":[58],"denoising":[59],"diffusion":[60],"probabilistic":[61,69],"model":[62],"(DDPM)-based":[63],"method,":[64],"termed":[65],"WorkloadDiff,":[66],"for":[67,149],"multivariate":[68],"prediction.":[71],"WorkloadDiff":[72,138],"leverages":[73],"both":[74],"original":[75],"noisy":[77],"signals":[78],"from":[79],"input":[80],"conditions":[81,112],"using":[82],"two-path":[84],"neural":[85],"network.":[86],"Additionally,":[87],"introduce":[89],"multi-scale":[91],"feature":[92],"extraction":[93],"method":[94],"an":[96],"adaptive":[97],"fusion":[98],"capture":[101],"diverse":[102],"temporal":[103],"patterns":[104],"within":[105],"workload.":[107],"enhance":[109],"consistency":[110],"between":[111],"predicted":[114],"values,":[115],"incorporate":[117],"resampling":[119],"strategy":[120],"into":[121],"inference":[123],"of":[124,137],"WorkloadDiff.":[125],"Extensive":[126],"experiments":[127],"conducted":[128],"on":[129],"four":[130],"public":[131],"datasets":[132],"demonstrate":[133],"superior":[135],"performance":[136],"over":[139],"all":[140],"baseline":[141],"models,":[142],"establishing":[143],"it":[144],"robust":[147],"tool":[148],"management":[151]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2025-10-10T00:00:00"}
