{"id":"W2115248402","doi":"10.1109/glocom.2005.1577728","title":"On optimizing token bucket parameters at the network edge under generalized processor sharing (GPS) scheduling","year":2005,"lang":"en","type":"article","venue":"GLOBECOM '05. IEEE Global Telecommunications Conference, 2005.","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Token bucket; Leaky bucket; Bounding overwatch; Computer science; Security token; Scheduling (production processes); Traffic shaping; Generalized processor sharing; Mathematical optimization; Computer network; Mathematics; Dynamic priority scheduling; Network traffic control; Quality of service; Round-robin scheduling","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":[0.001556603,0.0007851944,0.0009424588,0.000465333,0.0003617811,0.001039914,0.000903374,0.0008421066,0.001347781],"category_scores_gemma":[0.00537264,0.0003346172,0.0002197104,0.001000503,0.0008219841,0.002233464,0.0008567676,0.0006417505,0.000156292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001186865,"about_ca_system_score_gemma":0.001194435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002808613,"about_ca_topic_score_gemma":0.002039162,"domain_scores_codex":[0.9994543,0.0002208088,0.00001442116,0.00007752361,0.00008301593,0.0001499171],"domain_scores_gemma":[0.9982323,0.001327432,0.0001792445,0.00007752088,0.0001151167,0.00006851458],"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":[0.00007677051,0.00001236963,0.000159522,0.00002086939,0.000005146532,0.0000149079,0.00001481245,0.9864057,0.0009660609,0.003598862,0.0001462001,0.008578822],"study_design_scores_gemma":[0.00000974269,0.0000216575,0.00005578291,0.000002679171,0.000003837727,0.000004934968,0.00001695095,0.9958323,0.0006818396,0.003239836,0.0001268055,0.000003733528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1919027,0.0006172642,0.8036173,0.0002390183,0.00003182962,0.00006449484,0.00006382589,0.0002151428,0.00324857],"genre_scores_gemma":[0.93922,0.0003270224,0.0591997,0.0000456179,0.00001812735,0.00004256267,0.00004575209,0.00006494937,0.001036335],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002808613,"threshold_uncertainty_score":0.008611321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03630965501397243,"score_gpt":0.2765649439790216,"score_spread":0.2402552889650492,"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."}}