{"id":"W3129010358","doi":"10.1186/s13677-020-00223-5","title":"Generic SDE and GA-based workload modeling for cloud systems","year":2021,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Workload; Cloud computing; Computer science; Kalman filter; Overhead (engineering); Real-time computing; Distributed computing; Operating system; Artificial intelligence","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":[0.000899677,0.0008644604,0.0007751269,0.0006518535,0.0002994661,0.000848734,0.0008408725,0.0008426805,0.0009056756],"category_scores_gemma":[0.002543349,0.0004455379,0.0007821501,0.0005455589,0.0003749212,0.0007118149,0.00054243,0.0007526184,0.0002528848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000868537,"about_ca_system_score_gemma":0.0008227312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01061541,"about_ca_topic_score_gemma":0.00650721,"domain_scores_codex":[0.9995157,0.0001539335,0.00003080547,0.0001055384,0.0001231303,0.00007088058],"domain_scores_gemma":[0.9991875,0.0003792413,0.0001085441,0.0001002686,0.0001855025,0.00003898846],"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":[0.00001076073,0.00001728517,0.0003922308,0.00001473555,0.000009940228,0.00001271907,0.00001050044,0.9896138,0.0006335261,0.0007148458,0.00009006394,0.008479537],"study_design_scores_gemma":[7.199966e-7,0.000003962079,0.00006399478,9.882784e-7,6.946548e-7,0.000002263034,0.00000175643,0.9995089,0.0001491604,0.0002122573,0.0000543009,0.000001047671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03973996,0.0001616276,0.9575301,0.0001036413,0.00002765244,0.0000835303,0.0001105873,0.0005212172,0.00172163],"genre_scores_gemma":[0.8311563,0.000214176,0.1655745,0.00008641107,0.0000261178,0.0002212043,0.0003959141,0.00008214838,0.002243253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01061541,"threshold_uncertainty_score":0.02110726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02101204403142701,"score_gpt":0.2648088372187086,"score_spread":0.2437967931872816,"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."}}