{"id":"W4393928205","doi":"10.1145/3603166.3632131","title":"Cloud Workload Categorization Using Various Data Preprocessing and Clustering Techniques","year":2023,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Workload; Categorization; Cluster analysis; Cloud computing; Data pre-processing; Preprocessor; Data mining; Artificial intelligence; Machine learning; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005081011,0.00009417545,0.00009024253,0.0001274,0.0002431805,0.0003899343,0.0007610332,0.00003608121,0.000001054524],"category_scores_gemma":[0.00002449112,0.00008417237,0.0000103143,0.0006747085,0.00002093028,0.00009135496,0.003184987,0.00006793903,0.000006732083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002088366,"about_ca_system_score_gemma":0.00001883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001019207,"about_ca_topic_score_gemma":0.000009878394,"domain_scores_codex":[0.9989511,0.00003047513,0.0001561022,0.0004913695,0.0001603938,0.0002105094],"domain_scores_gemma":[0.999095,0.00003771052,0.00005744353,0.0007443691,0.00002366977,0.0000418121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003482629,0.00002806183,0.0008748072,0.0001291002,0.00002574955,0.00003494847,0.001564479,0.02983952,0.0007794479,0.002432471,0.0007985056,0.9634894],"study_design_scores_gemma":[0.00005197553,0.00001192445,0.0001903942,0.00007060648,0.000005031252,0.00001396235,0.00005514782,0.9970174,0.0002738031,0.0008745059,0.001316626,0.0001186001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04674082,0.00008461492,0.9495269,0.0003783874,0.0002006097,0.0001107536,2.677111e-7,0.001344873,0.00161276],"genre_scores_gemma":[0.894917,0.00001900581,0.1040249,0.000169661,0.0002017717,0.000002690258,0.000003318845,0.00001328547,0.0006484407],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9671779,"threshold_uncertainty_score":0.3969858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05473344315201446,"score_gpt":0.2956698916277382,"score_spread":0.2409364484757237,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}