{"id":"W2976431391","doi":"10.1109/tsc.2019.2944360","title":"Improving the Schedulability of Real-Time Tasks Using Fog Computing","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Cloud computing; Scheduling (production processes); Fog computing; Response time; Execution time; Schedule; Computation; Notation; Distributed computing; Parallel computing; Embedded system; Algorithm; Operating system; Arithmetic","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":[0.0007412498,0.0009000105,0.0006138181,0.0006246488,0.0006791526,0.0008166575,0.0009971666,0.0004640221,0.0009097739],"category_scores_gemma":[0.002282824,0.0002424852,0.0007254204,0.0004668247,0.0004373613,0.0007954179,0.0005644541,0.0005418179,0.0002583949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008725484,"about_ca_system_score_gemma":0.002443939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007474795,"about_ca_topic_score_gemma":0.006025406,"domain_scores_codex":[0.9993506,0.0001139636,0.00004677299,0.0001301982,0.0001605489,0.0001978455],"domain_scores_gemma":[0.9991967,0.0003271893,0.0001107656,0.0001221394,0.0001747018,0.00006849239],"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":[0.0007827152,0.0003386323,0.004069186,0.0005455396,0.0001210601,0.000631079,0.0003827774,0.6793863,0.0838021,0.01914455,0.004823468,0.2059727],"study_design_scores_gemma":[0.00002752902,0.00009244872,0.0006542103,0.00001834764,0.00001913572,0.00006726608,0.00004530033,0.9810522,0.01186702,0.004356007,0.00178603,0.00001467025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1355426,0.001406572,0.854695,0.0002346499,0.0002136164,0.0001979836,0.0001059348,0.001312314,0.006291316],"genre_scores_gemma":[0.8565807,0.0004345788,0.141439,0.00008860178,0.00006745729,0.0001091081,0.0001704551,0.0001047945,0.001005307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007474795,"threshold_uncertainty_score":0.01486254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01452352159092712,"score_gpt":0.2472570099561804,"score_spread":0.2327334883652532,"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."}}