{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005635509,0.00009131536,0.0001102046,0.00004293167,0.0006363098,0.0001674946,0.0004569312,0.00001887168,0.000008237472],"category_scores_gemma":[0.00001271495,0.00009109513,0.00001427793,0.000299626,0.0000175807,0.0000335045,0.003713633,0.0002364809,3.849918e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002480078,"about_ca_system_score_gemma":0.000007040816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002493981,"about_ca_topic_score_gemma":0.000003333698,"domain_scores_codex":[0.9988721,0.0001335051,0.000147072,0.0004234444,0.0001896375,0.0002342624],"domain_scores_gemma":[0.9994494,0.00004798924,0.00004964774,0.0003812689,0.00001110675,0.00006063791],"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":[0.000005759908,0.00001254194,0.002557277,0.000003572139,0.00001378588,0.000006924693,0.0001283561,0.9757714,1.012355e-7,0.0003844775,0.00008131751,0.02103444],"study_design_scores_gemma":[0.0001959072,0.0000281189,0.00002742495,0.00002018684,0.00001289253,0.00002437232,0.00002758402,0.9976005,2.092964e-8,0.0001205493,0.001838668,0.0001037624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05653539,0.001683179,0.9408562,0.0003495095,0.0002549771,0.00006899032,7.900463e-7,0.00008356551,0.0001673583],"genre_scores_gemma":[0.9045195,0.00004220997,0.09465682,0.0002891361,0.0003931285,0.00000142045,0.00001309044,0.00001283771,0.000071846],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8479841,"threshold_uncertainty_score":0.4894045,"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."}}