{"id":"W4212997113","doi":"10.1109/icnp52444.2021.9651972","title":"HLS: A Packet Scheduler for Hierarchical Fairness","year":2021,"lang":"en","type":"article","venue":"","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Linux kernel; Scheduling (production processes); Network packet; Computer network; Network scheduler; Overhead (engineering); Distributed computing; Bandwidth (computing); Temporal isolation among virtual machines; Embedded system; Operating system; Transmission delay; Processing delay","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":[],"consensus_categories":[],"category_scores_codex":[0.0001446419,0.00008095636,0.0001188224,0.00002244299,0.00008286032,0.0001384819,0.0003294312,0.00004985818,0.00009955922],"category_scores_gemma":[0.00005253567,0.00006872244,0.0000820039,0.0001986771,0.0000238527,0.0001590204,0.0000964263,0.00008172109,0.00006175876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009914551,"about_ca_system_score_gemma":0.0001377106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.882051e-7,"about_ca_topic_score_gemma":0.00000967548,"domain_scores_codex":[0.9991512,0.00004312244,0.0001281629,0.0003121681,0.0001320018,0.0002333546],"domain_scores_gemma":[0.999206,0.0002006461,0.00001976729,0.0003401913,0.000133988,0.00009942759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004200538,0.00003371518,0.00003936549,0.000003481509,0.00001151267,0.000009265918,0.00003311617,0.00005088536,0.00004418719,0.6340106,0.005167344,0.3605923],"study_design_scores_gemma":[0.001627214,0.00007875945,0.0005317845,0.00001944484,0.0000122486,0.00004812785,0.00004999305,0.7837108,0.001069255,0.04281207,0.1697141,0.0003262441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002590879,0.0001462348,0.9803475,0.01095523,0.0003990403,0.0001069372,0.000001286244,0.0002091087,0.005243771],"genre_scores_gemma":[0.8453756,0.000008198016,0.1454077,0.002818441,0.0002530988,0.00007178519,0.000004695296,0.000007132924,0.006053382],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8427847,"threshold_uncertainty_score":0.280242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01493202873890277,"score_gpt":0.2452504884303761,"score_spread":0.2303184596914733,"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."}}