{"id":"https://openalex.org/W4408146474","doi":"https://doi.org/10.1109/icmla61862.2024.00147","title":"Scheduled Sampling for Recursive Multi-Step GPU Temperature Forecasting","display_name":"Scheduled Sampling for Recursive Multi-Step GPU Temperature Forecasting","publication_year":2024,"publication_date":"2024-12-18","ids":{"openalex":"https://openalex.org/W4408146474","doi":"https://doi.org/10.1109/icmla61862.2024.00147"},"language":"en","primary_location":{"id":"doi:10.1109/icmla61862.2024.00147","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla61862.2024.00147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Machine Learning and Applications (ICMLA)","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/A5005703885","display_name":"Han Wang","orcid":"https://orcid.org/0000-0003-0824-2912"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Harold Wang","raw_affiliation_strings":["University of California, San Diego,Department of Cognitive Science,San Diego,CA,92092"],"affiliations":[{"raw_affiliation_string":"University of California, San Diego,Department of Cognitive Science,San Diego,CA,92092","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069666321","display_name":"Xunfei Jiang","orcid":"https://orcid.org/0000-0001-7934-0144"},"institutions":[{"id":"https://openalex.org/I157638225","display_name":"California State University, Northridge","ror":"https://ror.org/005f5hv41","country_code":"US","type":"education","lineage":["https://openalex.org/I157638225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xunfei Jiang","raw_affiliation_strings":["California State University, Northridge,Department of Computer Science,Northridge,CA,91330"],"affiliations":[{"raw_affiliation_string":"California State University, Northridge,Department of Computer Science,Northridge,CA,91330","institution_ids":["https://openalex.org/I157638225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100586616","display_name":"Mahdi Ebrahimi","orcid":null},"institutions":[{"id":"https://openalex.org/I157638225","display_name":"California State University, Northridge","ror":"https://ror.org/005f5hv41","country_code":"US","type":"education","lineage":["https://openalex.org/I157638225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mahdi Ebrahimi","raw_affiliation_strings":["California State University, Northridge,Department of Computer Science,Northridge,CA,91330"],"affiliations":[{"raw_affiliation_string":"California State University, Northridge,Department of Computer Science,Northridge,CA,91330","institution_ids":["https://openalex.org/I157638225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5005703885"],"corresponding_institution_ids":["https://openalex.org/I36258959"],"apc_list":null,"apc_paid":null,"fwci":0.37,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.6342377,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"991","last_page":"996"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.857699990272522,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.857699990272522,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.7551000118255615,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.7293999791145325,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.6647632718086243},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5287259221076965},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3433555066585541},{"id":"https://openalex.org/keywords/computational-science","display_name":"Computational science","score":0.34043896198272705},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.1601766049861908}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6647632718086243},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5287259221076965},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3433555066585541},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.34043896198272705},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.1601766049861908},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla61862.2024.00147","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla61862.2024.00147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/13"}],"awards":[{"id":"https://openalex.org/G6162561645","display_name":null,"funder_award_id":"CNS-2244391","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2162478414","https://openalex.org/W2268617045","https://openalex.org/W2979866182","https://openalex.org/W3040211378","https://openalex.org/W3107745730","https://openalex.org/W4320522363","https://openalex.org/W4327767743","https://openalex.org/W4385245566","https://openalex.org/W4390915112","https://openalex.org/W4392904973","https://openalex.org/W4392943679","https://openalex.org/W6731595287"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Energy-efficient":[0],"workload":[1,20,85],"management":[2,21],"is":[3],"increasingly":[4],"important":[5],"in":[6,19,124],"data":[7],"centers":[8],"as":[9,27],"demand":[10],"for":[11,75],"cloud":[12],"computing":[13],"continues":[14],"to":[15,23,31,39,59,81,128],"grow.":[16],"Recent":[17],"findings":[18],"point":[22],"thermal-aware":[24],"load":[25],"balancers":[26],"an":[28,69],"effective":[29],"approach":[30],"reduce":[32],"energy":[33,42],"consumption":[34],"via":[35],"cooling":[36],"costs.":[37],"Due":[38],"the":[40,101],"high":[41],"cost":[43],"of":[44,51],"Data":[45],"Centers":[46],"(DCs)":[47],"and":[48,106],"possible":[49],"risk":[50],"disrupting":[52],"critical":[53],"services,":[54],"simulations":[55],"can":[56],"be":[57],"used":[58],"evaluate":[60],"temperature-aware":[61,84],"load-balancing":[62],"algorithms.":[63],"In":[64],"this":[65],"paper,":[66],"we":[67,91],"leverage":[68],"existing":[70],"DC":[71],"simulator's":[72],"environmental":[73],"parameters":[74],"integrating":[76],"a":[77,83,93,111,122],"temperature":[78,114],"prediction":[79,115],"model":[80,98,120],"construct":[82],"distribution":[86],"simulation.":[87],"Using":[88],"scheduled":[89],"sampling,":[90],"implemented":[92],"custom":[94],"recursive":[95],"multi-step":[96],"forecasting":[97],"that":[99],"reduced":[100],"discrepancy":[102],"between":[103],"training":[104],"error":[105],"simulation":[107],"predictions.":[108],"Compared":[109],"with":[110],"baseline":[112],"GPU":[113],"model,":[116],"our":[117],"hybrid":[118],"CNN-LSTM":[119],"achieved":[121],"reduction":[123],"RMSE":[125],"from":[126],"2.54":[127],"0.73.":[129]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}