{"id":"W2027993342","doi":"10.1109/icdcsw.2013.69","title":"On Fairness-Efficiency Tradeoffs for Multi-resource Packet Processing","year":2013,"lang":"en","type":"article","venue":"","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Weighted fair queueing; Computer science; Generalized processor sharing; Network packet; Queueing theory; Fairness measure; Fair queuing; Computer network; Distributed computing; Scheduling (production processes); Bandwidth (computing); Schedule; Round-robin scheduling; Mathematical optimization; Throughput; Dynamic priority scheduling; Quality of service; Telecommunications; Operating system","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":[0.0001579075,0.0001501604,0.0001440452,0.00006047263,0.0002008132,0.0002909048,0.0006212447,0.00006184472,0.00005233953],"category_scores_gemma":[0.00004283552,0.0001145224,0.00007278229,0.0002231658,0.00003755345,0.0003381081,0.0000408989,0.00009257298,0.0001658661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001900729,"about_ca_system_score_gemma":0.00004970071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005786279,"about_ca_topic_score_gemma":0.000003112747,"domain_scores_codex":[0.9988095,0.00002916566,0.0001949832,0.0004049022,0.0001915946,0.0003698104],"domain_scores_gemma":[0.9992409,0.0001838629,0.00006407408,0.0003032898,0.0000897327,0.0001181892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004842155,0.0001394798,0.00001576831,0.00001000382,0.000004391023,7.719401e-7,0.0002696581,0.0009464723,0.000042229,0.0757312,0.008019274,0.9148159],"study_design_scores_gemma":[0.0008959955,0.0001163774,0.0002321056,0.00001938673,0.000003399215,0.000003035102,0.00006083714,0.9908623,0.0001019556,0.001225146,0.006295872,0.0001835711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005353882,0.00008585084,0.9874521,0.002706543,0.0001452235,0.0005894286,6.59341e-7,0.0004256896,0.00324058],"genre_scores_gemma":[0.9560112,9.101099e-7,0.0383585,0.001790561,0.00009450487,0.0002043302,0.000001525033,0.00001126824,0.003527214],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9899158,"threshold_uncertainty_score":0.4670089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02548469638332735,"score_gpt":0.2516465563336333,"score_spread":0.226161859950306,"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."}}