{"id":"W2970278883","doi":"10.1109/services.2019.00104","title":"A Systematic Cloud Workload Clustering Technique in Large Scale Data Centers","year":2019,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cluster analysis; Computer science; Workload; Cloud computing; Data mining; Scheduling (production processes); Virtual machine; Data center; CURE data clustering algorithm; Scale (ratio); Correlation clustering; Machine learning; Operating system; Engineering","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":[0.001215218,0.0001302428,0.000250561,0.0001500884,0.00004588829,0.0001569434,0.002371411,0.00004412854,0.00001171517],"category_scores_gemma":[0.00001613679,0.0001047566,0.00003973802,0.0004308486,0.00000842361,0.00005560759,0.003633243,0.0001369371,0.0001890791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005671131,"about_ca_system_score_gemma":0.00001205525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005464814,"about_ca_topic_score_gemma":0.00007472659,"domain_scores_codex":[0.9984058,0.0001124129,0.0003412703,0.0005315675,0.0002447782,0.0003642189],"domain_scores_gemma":[0.997813,0.00007024379,0.0000824997,0.001968701,0.00001290177,0.00005264015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00022601,0.008617824,0.2211046,0.2383454,0.001517782,0.001580738,0.05134254,0.2582269,0.006762223,0.08383857,0.0511824,0.07725496],"study_design_scores_gemma":[0.0003577998,0.00002472753,0.000530997,0.004399007,0.000004251428,0.00001600767,0.000301301,0.9936851,0.00004333519,0.00005837457,0.0003881352,0.0001910255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05986373,0.0001749374,0.9277887,0.0005220635,0.0006493808,0.001246264,0.000001169509,0.0004025358,0.009351167],"genre_scores_gemma":[0.9676581,0.000003066478,0.02982453,0.0003976983,0.00004432088,0.00002631013,0.000001722843,0.00001086893,0.002033379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9077944,"threshold_uncertainty_score":0.4528577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01356017232853781,"score_gpt":0.2383156732756435,"score_spread":0.2247555009471057,"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."}}