{"id":"W3184805929","doi":"","title":"{SKQ}: Event Scheduling for Optimizing Tail Latency in a Traditional {OS} Kernel","year":2021,"lang":"en","type":"article","venue":"USENIX Annual Technical Conference","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Latency (audio); Kernel (algebra); Scheduling (production processes); Distributed computing; Parallel computing; Mathematical optimization; Mathematics; Telecommunications","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":[0.001318436,0.001081596,0.0004673652,0.0005780212,0.0006205905,0.001098822,0.002501466,0.0004427107,0.006378688],"category_scores_gemma":[0.003293263,0.0004571119,0.0003616681,0.0005830058,0.0005777128,0.001224385,0.001166111,0.001219534,0.00128048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009926684,"about_ca_system_score_gemma":0.002202545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005541339,"about_ca_topic_score_gemma":0.01084074,"domain_scores_codex":[0.9992467,0.0001278377,0.00006470172,0.0001575163,0.0002229062,0.0001804577],"domain_scores_gemma":[0.9984152,0.0003737216,0.0001231904,0.0006074578,0.0002965632,0.0001838006],"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":[0.006876756,0.001169557,0.01327839,0.0004980738,0.0002372526,0.0002696137,0.0004346809,0.1492151,0.1317671,0.0240436,0.0531253,0.6190847],"study_design_scores_gemma":[0.0003503408,0.00043017,0.001680048,0.00001645361,0.00009009237,0.00006929144,0.00005127579,0.9064914,0.07298411,0.006927341,0.01085815,0.00005127917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1662615,0.0003867646,0.751801,0.0003618693,0.0004217452,0.0001823983,0.0006722291,0.07346155,0.006450892],"genre_scores_gemma":[0.777396,0.0001040346,0.2122422,0.0002317896,0.00009965514,0.00008101125,0.0005846025,0.003322275,0.005938562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006378688,"threshold_uncertainty_score":0.02133882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05502411700400726,"score_gpt":0.2966008762843377,"score_spread":0.2415767592803305,"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."}}