{"id":"W2971627722","doi":"10.1109/jiot.2019.2939534","title":"KEIDS: Kubernetes-Based Energy and Interference Driven Scheduler for Industrial IoT in Edge-Cloud Ecosystem","year":2019,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":161,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Cloud computing; Computer science; Interference (communication); Enhanced Data Rates for GSM Evolution; Internet of Things; Ecosystem; Telecommunications; Computer security; Operating system; Channel (broadcasting); Ecology","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":[0.0009442511,0.0002244893,0.000426875,0.0003551627,0.00004636603,0.0003266377,0.001217268,0.0001774326,0.00001013181],"category_scores_gemma":[0.0001042223,0.0002006097,0.000147984,0.0001857733,0.00003663317,0.0004470597,0.0002258674,0.0004755323,0.000008571798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001284825,"about_ca_system_score_gemma":0.000175465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008994793,"about_ca_topic_score_gemma":0.00001120518,"domain_scores_codex":[0.9980909,0.000124416,0.000709742,0.0003784876,0.000276984,0.0004195056],"domain_scores_gemma":[0.9985708,0.0003355055,0.0005066972,0.0002652963,0.0001823971,0.0001393106],"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":[0.002684321,0.001498325,0.1807747,0.001345581,0.001147448,0.0003383966,0.03559543,0.01145581,0.04937396,0.01959416,0.1349422,0.5612497],"study_design_scores_gemma":[0.003320342,0.0009382904,0.0002171243,0.001944369,0.0000150225,0.0001995792,0.00004696213,0.9532278,0.0276921,0.001816439,0.0101511,0.0004308717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7580649,0.0001009973,0.2246673,0.0002582959,0.01639658,0.0001420912,4.388097e-7,0.00003276963,0.0003365641],"genre_scores_gemma":[0.988779,0.000003137145,0.009081557,0.0002320126,0.001616733,0.000004233751,6.627724e-7,0.00001780445,0.0002648479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.941772,"threshold_uncertainty_score":0.8180625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02524107272682274,"score_gpt":0.2378335635533396,"score_spread":0.2125924908265168,"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."}}