{"id":"W4311853480","doi":"10.3390/fi14120368","title":"Holistic Utility Satisfaction in Cloud Data Centre Network Using Reinforcement Learning","year":2022,"lang":"en","type":"article","venue":"Future Internet","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Iran Telecommunication Research Center","keywords":"Computer science; Cloud computing; Reinforcement learning; Distributed computing; Resource allocation; Service provider; Mathematical optimization; Computer network; Service (business); Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007583051,0.0001351566,0.0001466059,0.00007895031,0.0002052973,0.0001163014,0.001211946,0.00003026084,0.0001372564],"category_scores_gemma":[0.00002196052,0.0001372498,0.00003759718,0.0004028292,0.00001462894,0.00004359297,0.003918705,0.0005061168,0.00001169863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000206092,"about_ca_system_score_gemma":0.00002719541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009029313,"about_ca_topic_score_gemma":0.0001501514,"domain_scores_codex":[0.9982312,0.000260946,0.0002865656,0.0005218852,0.0003544205,0.0003449659],"domain_scores_gemma":[0.9989523,0.00004607939,0.0001405111,0.0007993969,0.0000158094,0.00004593009],"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":[0.00001860245,0.00002857806,0.01939602,0.00001708207,0.00002454664,0.00004827525,0.001468412,0.925106,0.000003480043,0.003214044,0.0152399,0.03543502],"study_design_scores_gemma":[0.0001837648,0.00003357096,0.006159252,0.00002356695,0.000005705198,0.00001239783,0.0003701359,0.8341979,0.000001413841,0.0001315239,0.1587559,0.0001248789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6508645,0.0004219236,0.3358689,0.001087779,0.008824138,0.0003844468,0.000004289486,0.0003370298,0.002206991],"genre_scores_gemma":[0.9958476,0.000001798481,0.002146453,0.0003228398,0.0009578697,0.000003060222,0.00002538596,0.000008922015,0.0006860345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3449831,"threshold_uncertainty_score":0.5596884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04069006900267534,"score_gpt":0.2685331898582127,"score_spread":0.2278431208555374,"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."}}