{"id":"W4416850843","doi":"10.2139/ssrn.5709722","title":"PPO-RA: A Proximal Policy Optimization-Based Deep Reinforcement Learning Framework for Adaptive Resource Allocation in Cloud-Edge-Mist Environments","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Marriott International (Canada)","funders":"","keywords":"Reinforcement learning; Scalability; Resource allocation; Scheduling (production processes); Workload; Cloud computing; Energy consumption; Task (project management); Resource management (computing)","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":[0.001442795,0.0008959933,0.001586741,0.000380673,0.0004434391,0.001003677,0.002762269,0.00188884,0.004641582],"category_scores_gemma":[0.003390363,0.000643272,0.0005188548,0.00048205,0.0008841125,0.001125064,0.002080016,0.002950403,0.0008273046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009115491,"about_ca_system_score_gemma":0.002704279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01017891,"about_ca_topic_score_gemma":0.01122244,"domain_scores_codex":[0.999472,0.0001724278,0.00001990095,0.0001146005,0.0001079897,0.0001131984],"domain_scores_gemma":[0.9990607,0.000514414,0.00006240368,0.00007119871,0.0001716665,0.0001196449],"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":[0.00009968097,0.00009540278,0.0002820904,0.00005356206,0.00003608079,0.00004145371,0.00002791385,0.9453912,0.0006791176,0.008289227,0.002264133,0.04274016],"study_design_scores_gemma":[0.000004965833,0.000009126758,0.00001157711,0.000002036724,0.000001640284,0.000002621426,0.000001290387,0.9985405,0.00006142923,0.001239623,0.0001237755,0.000001428063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007916362,0.0003204717,0.9880772,0.0002649722,0.0001050529,0.00004992549,0.00006307154,0.0008499948,0.002353133],"genre_scores_gemma":[0.7020064,0.0003896962,0.2868327,0.0005971789,0.000157994,0.0002963287,0.0002420256,0.000317547,0.009160137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01017891,"threshold_uncertainty_score":0.02023929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009330637372290167,"score_gpt":0.2476262314536562,"score_spread":0.238295594081366,"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."}}