{"id":"W4388383276","doi":"10.1016/j.comnet.2023.110088","title":"Multi-resource predictive workload consolidation approach in virtualized environments","year":2023,"lang":"en","type":"article","venue":"Computer Networks","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Workload; Service-level agreement; Virtualization; Server; Energy consumption; Service provider; Distributed computing; Host (biology); Service level; Data center; Consolidation (business); Computer network; Operating system; Service (business)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001194535,0.0005289365,0.001090446,0.0005910739,0.0005081688,0.00143531,0.001815051,0.0006378839,0.001133553],"category_scores_gemma":[0.002654238,0.000480616,0.0003585542,0.0009838898,0.0005228699,0.001412276,0.0007978319,0.0008032159,0.0001738033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006947092,"about_ca_system_score_gemma":0.001094499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006489087,"about_ca_topic_score_gemma":0.006354793,"domain_scores_codex":[0.9993722,0.0001945018,0.00003119507,0.0001120995,0.0001634573,0.0001265833],"domain_scores_gemma":[0.9988839,0.0005005137,0.0001261977,0.0001140572,0.0002743541,0.0001009485],"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":[0.0001278996,0.00009249353,0.001277872,0.00004471987,0.00004361841,0.00009314672,0.00005048873,0.9517259,0.001726466,0.004791729,0.0008008791,0.03922487],"study_design_scores_gemma":[0.000001047525,0.00000547228,0.00004434287,0.00000101615,0.000002539261,0.000004900676,0.000004588537,0.999385,0.00007051239,0.000445733,0.00003404175,8.47145e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.078936,0.0009526403,0.9160715,0.000380584,0.0001525728,0.00007631294,0.00005300722,0.000433981,0.002943469],"genre_scores_gemma":[0.9624798,0.0002753029,0.03603191,0.00007988053,0.0000691462,0.00003893103,0.00004599567,0.00003480914,0.0009442755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006489087,"threshold_uncertainty_score":0.01290268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01783923817551811,"score_gpt":0.2283054835715666,"score_spread":0.2104662453960485,"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."}}