{"id":"W4309741880","doi":"10.3390/network2040037","title":"Cloud Workload and Data Center Analytical Modeling and Optimization Using Deep Machine Learning","year":2022,"lang":"en","type":"article","venue":"Network","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Workload; Data center; Provisioning; Computer science; Scaling; Cloud computing; Machine learning; Artificial intelligence; Data mining; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005741417,0.0009293606,0.0005480614,0.0004407659,0.0003556174,0.0007823246,0.000631294,0.0005995119,0.001039712],"category_scores_gemma":[0.002126219,0.0004192767,0.0004589973,0.0004703236,0.000399412,0.0008982368,0.0003421399,0.0009998431,0.0001839717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00143185,"about_ca_system_score_gemma":0.001659147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01402877,"about_ca_topic_score_gemma":0.01188588,"domain_scores_codex":[0.9996772,0.00007963783,0.00001646323,0.00007108343,0.00008456312,0.00007105015],"domain_scores_gemma":[0.999078,0.0005077134,0.0001181311,0.00008153247,0.0001760301,0.00003848667],"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":[0.00001329308,0.00003083118,0.0006179493,0.000009341262,0.000004825927,0.000008607289,0.000006406455,0.9932241,0.0007288089,0.0004602796,0.0001137344,0.004781818],"study_design_scores_gemma":[3.855154e-7,0.000002593809,0.00005801269,5.06012e-7,3.745506e-7,8.635814e-7,0.000001344942,0.9995269,0.0001962359,0.0001925394,0.00001976506,5.911944e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.33801,0.0003669501,0.6515201,0.000779112,0.00005407929,0.0001039696,0.0002903278,0.001307876,0.007567634],"genre_scores_gemma":[0.9627642,0.0001245995,0.03542908,0.00006231426,0.00001504387,0.00006043641,0.000164766,0.0000486307,0.001330857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01402877,"threshold_uncertainty_score":0.02789426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03518092116170888,"score_gpt":0.2594411131870507,"score_spread":0.2242601920253418,"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."}}