{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00119519,0.000740825,0.001404329,0.0002857249,0.0003991271,0.001166446,0.001076831,0.0009298495,0.001830125],"category_scores_gemma":[0.002518579,0.0003422638,0.0005877281,0.0004398398,0.0009751641,0.0008150681,0.001140447,0.001283512,0.0001405809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001583246,"about_ca_system_score_gemma":0.00143065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01241994,"about_ca_topic_score_gemma":0.006445424,"domain_scores_codex":[0.9993537,0.0002328218,0.00002167508,0.0001344208,0.000119962,0.0001373871],"domain_scores_gemma":[0.9987883,0.000777976,0.0001220499,0.00003744691,0.0001809973,0.00009329459],"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":[0.00003438718,0.00002954735,0.0004277966,0.00002663319,0.00001392501,0.00005768299,0.00002486837,0.9895885,0.0003323592,0.003397304,0.0002745657,0.005792364],"study_design_scores_gemma":[0.00000248551,0.000006432128,0.00003018563,0.000001055663,0.000001392017,0.000002667282,0.000002655399,0.9993111,0.00003331292,0.0005732786,0.0000342389,0.000001210873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06928637,0.0005269958,0.923503,0.0004491315,0.00005659998,0.00008647949,0.0000439988,0.000230659,0.005816747],"genre_scores_gemma":[0.9766843,0.0001438954,0.02118102,0.00008214135,0.00001690622,0.00006630171,0.00003140344,0.00002107254,0.0017729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01241994,"threshold_uncertainty_score":0.02469528,"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."}}