{"id":"W4226019773","doi":"10.1109/jiot.2022.3205051","title":"Reinforcement Learning Framework for Server Placement and Workload Allocation in Multiaccess Edge Computing","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Server; Computer science; Reinforcement learning; Edge computing; Markov decision process; Distributed computing; Cloud computing; Enhanced Data Rates for GSM Evolution; Optimization problem; Computer network; Markov process; 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":[0.001609497,0.0009791175,0.001520545,0.0004862593,0.0004634371,0.001120873,0.002288464,0.001351613,0.00403575],"category_scores_gemma":[0.003313046,0.0005259975,0.000700075,0.0005724172,0.001162168,0.001187618,0.001306386,0.001970851,0.0004714567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001822043,"about_ca_system_score_gemma":0.002146814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01139278,"about_ca_topic_score_gemma":0.00928911,"domain_scores_codex":[0.9990532,0.0003140063,0.00003999889,0.000228312,0.0001828889,0.0001815673],"domain_scores_gemma":[0.9984983,0.0008786474,0.0001602226,0.00006734947,0.0002437491,0.000151724],"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":[0.00004542866,0.00004630893,0.0003519178,0.00003784905,0.00002042232,0.00005813989,0.00003775741,0.9693861,0.0004110944,0.01526023,0.0006552719,0.01368935],"study_design_scores_gemma":[0.00000504766,0.000007852798,0.0000193692,0.000001557832,0.000001700123,0.000002921103,0.000002499004,0.9971374,0.00004005001,0.002662438,0.0001175952,0.000001558079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0110518,0.0002870976,0.9852411,0.0003561655,0.00005409116,0.00005981091,0.00005692411,0.0002666747,0.002626221],"genre_scores_gemma":[0.8768795,0.0003801823,0.1147027,0.0002582099,0.00010471,0.0003210686,0.0001396417,0.00007917624,0.007134862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01139278,"threshold_uncertainty_score":0.02265292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02349005077857558,"score_gpt":0.2855333825615915,"score_spread":0.2620433317830159,"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."}}