{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00180968,0.000502574,0.0005577756,0.0007249536,0.0006711141,0.000932203,0.00245264,0.0004679408,0.006415717],"category_scores_gemma":[0.00382919,0.0003357134,0.0003421458,0.0005620728,0.0006547619,0.001011303,0.001293971,0.001164796,0.001477396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001133148,"about_ca_system_score_gemma":0.002563473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004649268,"about_ca_topic_score_gemma":0.003154434,"domain_scores_codex":[0.9991386,0.0001401947,0.0000759571,0.0001067127,0.0003611024,0.0001773211],"domain_scores_gemma":[0.9981691,0.0005033414,0.0001845945,0.0005052859,0.0004005032,0.0002372274],"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":[0.002933647,0.0009435714,0.007277246,0.0005317439,0.0002115048,0.0003376184,0.0007152516,0.1648348,0.09956603,0.06332108,0.08574348,0.573584],"study_design_scores_gemma":[0.0004400807,0.0003806397,0.001549929,0.00001967593,0.00005612826,0.0001217641,0.00004075182,0.8970847,0.04766281,0.009104576,0.04346299,0.00007595812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05840058,0.0004076587,0.8700159,0.0002040714,0.0004470972,0.0006965558,0.00079979,0.0612681,0.007760391],"genre_scores_gemma":[0.7137752,0.0002199559,0.269308,0.0003728355,0.0003329068,0.0005109755,0.001398685,0.00220941,0.01187209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006415717,"threshold_uncertainty_score":0.02146274,"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."}}