{"id":"https://openalex.org/W7127094173","doi":"https://doi.org/10.1051/epjconf/202533701120/pdf","title":"Decode the Workload: Training Deep Learning Models for Efficient Compute Cluster Representation","display_name":"Decode the Workload: Training Deep Learning Models for Efficient Compute Cluster Representation","publication_year":2025,"publication_date":"2025-10-07","ids":{"openalex":"https://openalex.org/W7127094173","doi":"https://doi.org/10.1051/epjconf/202533701120/pdf"},"language":"en","primary_location":{"id":"pmh:oai:edpsciences.org:dkey/10.1051/epjconf/202533701120","doi":"https://doi.org/10.1051/epjconf/202533701120/pdf","is_oa":false,"landing_page_url":"https://www.epj-conferences.org/10.1051/epjconf/202533701120/pdf","pdf_url":"https://www.epj-conferences.org/10.1051/epjconf/202533701120/pdf","source":{"id":"https://openalex.org/S4306400744","display_name":"Springer Link (Chiba Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I8488066","host_organization_name":"Chiba Institute of Technology","host_organization_lineage":["https://openalex.org/I8488066"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1051/epjconf/202533701120","raw_type":"Text"},"volume":null,"type":"article","indexed_in":[],"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/A5053325630","display_name":"Ahmed Hossam Mohammed","orcid":"https://orcid.org/0000-0002-6427-3003","observed_orcids":["https://orcid.org/0000-0002-6427-3003"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmed Hossam Mohammed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124851213","display_name":"Mark Jones","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mark Jones","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019413231","display_name":"Diana McSpadden","orcid":"https://orcid.org/0000-0002-8520-1631","observed_orcids":["https://orcid.org/0000-0002-8520-1631"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Diana McSpadden","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101414524","display_name":"M. Schram","orcid":"https://orcid.org/0000-0002-3475-2871","observed_orcids":["https://orcid.org/0000-0002-3475-2871","https://orcid.org/0000-0003-0515-4124"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Malachi Schram","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005916589","display_name":"Bryan Hess","orcid":"https://orcid.org/0000-0003-3391-7045","observed_orcids":["https://orcid.org/0000-0003-3391-7045"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bryan Hess","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5043233932","display_name":"Kishansingh Rajput","orcid":"https://orcid.org/0000-0002-4430-9937","observed_orcids":["https://orcid.org/0000-0002-4430-9937"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kishansingh Rajput","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46671863,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T12127","display_name":"Software System Performance and Reliability","score":0.7523999810218811,"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":0.7523999810218811,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.08709999918937683,"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.06520000100135803,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/workload-analysis","display_name":"workload characterization","score":0.48899999260902405},{"id":"https://openalex.org/keywords/autoencoder","display_name":"autoencoder","score":0.7300000190734863},{"id":"https://openalex.org/keywords/graph-neural-networks","display_name":"graph neural networks","score":0.7829999923706055},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"anomaly detection","score":0.7480000257492065},{"id":"https://openalex.org/keywords/self-supervised-learning","display_name":"self-supervised learning","score":0.8119999766349792},{"id":"https://openalex.org/keywords/foundation-models","display_name":"foundation models","score":0.6890000104904175},{"id":"https://openalex.org/keywords/high-throughput-computing","display_name":"high-throughput computing","score":0.7400000095367432}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8044999837875366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6442000269889832},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6255999803543091},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6043999791145325},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4968999922275543},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48910000920295715},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44369998574256897},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.44130000472068787},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.42649999260902405},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42410001158714294},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4235000014305115},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.39820000529289246},{"id":"https://openalex.org/C29140674","wikidata":"https://www.wikidata.org/wiki/Q206637","display_name":"Computer cluster","level":2,"score":0.39169999957084656},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31850001215934753},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.3059000074863434},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3034000098705292},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28690001368522644},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.2759999930858612},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C553261973","wikidata":"https://www.wikidata.org/wiki/Q14579","display_name":"Linux kernel","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:edpsciences.org:dkey/10.1051/epjconf/202533701120","doi":"https://doi.org/10.1051/epjconf/202533701120/pdf","is_oa":false,"landing_page_url":"https://www.epj-conferences.org/10.1051/epjconf/202533701120/pdf","pdf_url":"https://www.epj-conferences.org/10.1051/epjconf/202533701120/pdf","source":{"id":"https://openalex.org/S4306400744","display_name":"Springer Link (Chiba Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I8488066","host_organization_name":"Chiba Institute of Technology","host_organization_lineage":["https://openalex.org/I8488066"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1051/epjconf/202533701120","raw_type":"Text"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.5103581547737122}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,48],"this":[1],"study,":[2],"we":[3,50],"address":[4],"the":[5,69,76],"mounting":[6],"challenge":[7],"of":[8,29,79],"monitoring":[9],"high":[10],"throughput":[11],"computing":[12,113],"clusters":[13],"running":[14],"computationally":[15],"intensive":[16],"jobs,":[17],"which":[18],"increasingly":[19],"strains":[20],"system":[21,37],"administrators.":[22],"We":[23,66],"develop":[24],"autoencoders":[25],"that":[26],"analyze":[27],"traces":[28],"Linux":[30],"kernel":[31],"CPU":[32],"metrics":[33],"to":[34,55,90],"capture":[35],"salient":[36],"features":[38],"by":[39,74],"producing":[40],"robust":[41],"compressed":[42],"embeddings":[43],"for":[44,112],"various":[45],"downstream":[46],"tasks.":[47],"addition,":[49],"employ":[51],"graph":[52],"neural":[53],"networks":[54],"incorporate":[56],"contextual":[57],"information":[58],"from":[59],"surrounding":[60],"CPUs":[61],"and":[62,97],"assess":[63],"their":[64,88],"performance.":[65],"also":[67],"demonstrate":[68],"enhanced":[70],"job":[71,101],"differentiation":[72],"achieved":[73],"increasing":[75],"sampling":[77],"rate":[78],"these":[80],"traces.":[81],"Our":[82],"models":[83,111],"are":[84],"evaluated":[85],"based":[86],"on":[87],"ability":[89],"generate":[91],"meaningful":[92],"latent":[93],"representations,":[94],"detect":[95],"anomalies,":[96],"distinguish":[98],"between":[99],"different":[100],"types,":[102],"marking":[103],"a":[104],"preliminary":[105],"step":[106],"towards":[107],"self-supervised,":[108],"large-scale":[109],"foundation":[110],"centers.":[114]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2026-02-03T00:00:00"}