{"id":"W4415624267","doi":"10.1109/tnse.2025.3626049","title":"Model Predictive Congestion Control for Data Center Networks","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Queue; Model predictive control; Network congestion; Data center; Flow control (data); Ranging; Queueing theory; Control (management); Flow (mathematics); Online algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001852141,0.0003451854,0.0003249355,0.0003422717,0.001262891,0.0006042368,0.001628892,0.0001321092,0.000001063595],"category_scores_gemma":[0.00002658758,0.000350961,0.00008070066,0.001655154,0.0002918376,0.0002553368,0.00005801661,0.0004355224,0.000001245265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001710311,"about_ca_system_score_gemma":0.0002613931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009369109,"about_ca_topic_score_gemma":0.000006003535,"domain_scores_codex":[0.996906,0.00003038091,0.0004186716,0.001235024,0.0004513871,0.0009585164],"domain_scores_gemma":[0.9980358,0.0003149969,0.00008435518,0.001122169,0.0002289849,0.0002137292],"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":[0.00004898699,0.00007451095,0.000007149421,0.00005039217,0.00007970291,0.00000108427,0.00009203993,0.9064336,0.00002228525,0.001304804,0.0007824929,0.09110298],"study_design_scores_gemma":[0.001064132,0.0001471118,0.00009125365,0.0005186941,0.000123481,0.000003942219,0.00001631951,0.9965469,0.00001758984,0.0000840842,0.001086505,0.0002999877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001448374,0.0005120759,0.9912794,0.0009218513,0.004578951,0.000800789,0.00003378995,0.00020153,0.000223274],"genre_scores_gemma":[0.9895928,0.0001576964,0.009059519,0.0005485969,0.0002586951,0.00006077614,0.000001719506,0.0000167084,0.0003034732],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9881445,"threshold_uncertainty_score":0.9998943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0172838591951131,"score_gpt":0.2316578690657882,"score_spread":0.2143740098706751,"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."}}