{"id":"W7128765532","doi":"10.1109/iceca66444.2025.11382616","title":"Optimizing Cloud Resource Allocation using Reinforcement Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Cloud computing; Reinforcement learning; Resource allocation; Workload; Resource management (computing); Heuristic; Automation; Energy consumption","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":[0.001027384,0.0005789613,0.0006740492,0.0002949234,0.0002716447,0.0005960563,0.0008000771,0.0005866027,0.0006557913],"category_scores_gemma":[0.003072062,0.0002373065,0.0002440314,0.0002790499,0.0006639134,0.0006682064,0.0005892302,0.0008020759,0.000128729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008478404,"about_ca_system_score_gemma":0.001221619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006738441,"about_ca_topic_score_gemma":0.004719693,"domain_scores_codex":[0.9995066,0.0001964834,0.00001875973,0.00009372317,0.00009898686,0.00008546606],"domain_scores_gemma":[0.9987857,0.000790882,0.0001533538,0.00006325761,0.0001449729,0.00006178495],"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":[0.0000363838,0.00005138818,0.0006039583,0.00001805485,0.00001632248,0.00002496676,0.00001516256,0.9793015,0.001042919,0.001555178,0.0002689648,0.01706522],"study_design_scores_gemma":[0.000005160945,0.0000114762,0.00004452912,0.000001176095,0.000001551415,0.000002632737,0.000001882959,0.9989306,0.0001621415,0.0007840191,0.0000533495,0.000001424974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0858366,0.0003198658,0.909556,0.0004153573,0.00004194214,0.00009245209,0.00002801467,0.0006685836,0.003041098],"genre_scores_gemma":[0.9688719,0.00009726656,0.03015508,0.00007857446,0.00001993291,0.00005016288,0.00002228322,0.00001925339,0.000685591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006738441,"threshold_uncertainty_score":0.01339847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02004849382064382,"score_gpt":0.2589497856891222,"score_spread":0.2389012918684784,"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."}}