{"id":"W2551184441","doi":"10.1109/tcc.2016.2628891","title":"<i>Adia</i>: Achieving High Link Utilization with Coflow-Aware Scheduling in Data Center Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Cloud Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Distributed computing; Scheduling (production processes); Dynamic priority scheduling; Round-robin scheduling; Two-level scheduling; Fair-share scheduling; Computer network; Mathematical optimization; Quality of service","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":[0.001456204,0.0006938897,0.0005903707,0.000931423,0.001198474,0.00113723,0.001448304,0.0005899489,0.00106811],"category_scores_gemma":[0.002537949,0.0002778566,0.0003756312,0.001154977,0.000865886,0.001407077,0.001504894,0.001046018,0.0002331219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001563235,"about_ca_system_score_gemma":0.002015354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003865546,"about_ca_topic_score_gemma":0.004435584,"domain_scores_codex":[0.999301,0.0002162759,0.00003896322,0.0001255298,0.0001804879,0.0001377908],"domain_scores_gemma":[0.9986762,0.0005531982,0.0001504373,0.0002344988,0.0002320705,0.0001535599],"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":[0.0003229957,0.0002245215,0.002201598,0.0001861471,0.00006920898,0.0001442713,0.0002028091,0.7196886,0.02045225,0.04817665,0.00847723,0.1998537],"study_design_scores_gemma":[0.00001049183,0.00004142022,0.0001743462,0.000006519743,0.000009804259,0.0000433389,0.00002269074,0.9844253,0.004977165,0.008046175,0.002234309,0.000008550146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02262521,0.0005015144,0.9734846,0.0002776184,0.00008641908,0.00006149052,0.00004159118,0.0007324014,0.002189277],"genre_scores_gemma":[0.6944927,0.0004369681,0.3033152,0.0001800005,0.0001132488,0.0000986243,0.00010528,0.0001088743,0.001149041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003865546,"threshold_uncertainty_score":0.01134211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03470422745748893,"score_gpt":0.2540282689419918,"score_spread":0.2193240414845028,"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."}}