{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0009537711,0.0005838021,0.0005568996,0.0000840834,0.00177181,0.0009307023,0.00537575,0.000258864,0.0001929911],"category_scores_gemma":[0.00006795697,0.0004981786,0.0002709286,0.0009741611,0.0002797666,0.0006923671,0.001037037,0.0007171782,0.0005471787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000715678,"about_ca_system_score_gemma":0.0004319155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009804344,"about_ca_topic_score_gemma":0.001621709,"domain_scores_codex":[0.9958081,0.0004346922,0.0009170384,0.00097233,0.0005565851,0.001311298],"domain_scores_gemma":[0.9951551,0.0005028262,0.0004741446,0.003195113,0.0003091086,0.0003637226],"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":[0.00005385789,0.0001927749,0.0002158431,0.000006889115,0.0001635477,0.000003109016,0.0001296805,0.6326803,0.00001848187,0.216033,0.03246374,0.1180387],"study_design_scores_gemma":[0.001736447,0.00008904908,0.0003807539,0.0001514344,0.00008525272,0.00007691602,0.00007815197,0.9364707,0.00007854719,0.009892444,0.05012857,0.0008317407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.10827,0.003689901,0.7993224,0.04835697,0.001970973,0.001937764,0.00006883754,0.001535992,0.0348472],"genre_scores_gemma":[0.864261,0.0007963073,0.1263398,0.006324568,0.0003658753,0.0003114768,0.00005796615,0.00003280337,0.001510244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.755991,"threshold_uncertainty_score":0.999747,"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."}}