{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004073062,0.00021872,0.0003683981,0.0002077546,0.0001238335,0.00007841863,0.0007373883,0.0001526006,0.00002099687],"category_scores_gemma":[0.00000277933,0.0002226886,0.0001483091,0.0009925417,0.00002527613,0.0002146329,0.00001290475,0.000482549,0.00004342866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004582617,"about_ca_system_score_gemma":0.0000498325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001131477,"about_ca_topic_score_gemma":0.0000311767,"domain_scores_codex":[0.9981771,0.00008789987,0.0005234237,0.0004975921,0.0002651707,0.0004488267],"domain_scores_gemma":[0.998589,0.0005075952,0.0001859705,0.0005623371,0.0000777981,0.00007730463],"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":[0.00002088434,0.0001245222,0.00157781,0.00006622645,0.00002106234,0.00000474869,0.0004279924,0.9554038,0.0001473035,0.0003769078,0.000002450015,0.04182627],"study_design_scores_gemma":[0.0008717323,0.00009385607,0.001174892,0.0004656705,0.000006690753,0.00000720311,0.00003515821,0.9957919,0.0009578118,0.0003532273,0.0000138446,0.0002279957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3536643,0.00009270162,0.6441923,0.0000467453,0.001583041,0.0001514913,7.483902e-7,0.000195312,0.00007342183],"genre_scores_gemma":[0.9648885,0.00002305318,0.03472297,0.0002146134,0.00009467836,0.000004047944,8.395172e-7,0.00002074436,0.00003054952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6112242,"threshold_uncertainty_score":0.9080977,"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."}}