{"id":"W2926281209","doi":"10.1109/tcc.2019.2907949","title":"Scheduling of Low Latency Services in Softwarized Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Cloud Computing","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Computer science; Scheduling (production processes); Latency (audio); Cloud computing; Distributed computing; Computer network; Operating system; Telecommunications; Mathematical optimization","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":[0.001340039,0.0007700481,0.0007830667,0.0003337614,0.0006765578,0.001669054,0.001331969,0.0008057253,0.001915985],"category_scores_gemma":[0.002743933,0.0003943904,0.0003944421,0.0004785627,0.0009433131,0.0015831,0.001271387,0.0009448653,0.0001995804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001972144,"about_ca_system_score_gemma":0.001889498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005998274,"about_ca_topic_score_gemma":0.005740245,"domain_scores_codex":[0.9987125,0.0004953506,0.00004944437,0.0002167461,0.0002235345,0.0003024231],"domain_scores_gemma":[0.9985904,0.0007988653,0.0001772982,0.00008125296,0.0001488491,0.0002033777],"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":[0.0002935813,0.00006598687,0.0003950127,0.0001002202,0.00003464421,0.0001898486,0.0001519044,0.9125951,0.004544463,0.06473437,0.001299237,0.01559566],"study_design_scores_gemma":[0.00001214055,0.00002659634,0.0000629343,0.000004142789,0.000004113754,0.00001209082,0.00003631062,0.9871438,0.0003684223,0.01166205,0.0006628092,0.000004579273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1698907,0.0007098377,0.8177367,0.0008041629,0.0002105236,0.0002566102,0.0001839755,0.000289183,0.009918327],"genre_scores_gemma":[0.9597732,0.0002947621,0.03655928,0.0001132272,0.00005285807,0.0001009853,0.00006052988,0.00004256945,0.003002533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005998274,"threshold_uncertainty_score":0.01430893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00815616455129771,"score_gpt":0.2203700959305087,"score_spread":0.212213931379211,"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."}}