{"id":"W4394876553","doi":"10.1016/j.comcom.2024.04.009","title":"Reinforcement learning-based dynamic load balancing in edge computing networks","year":2024,"lang":"en","type":"article","venue":"Computer Communications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Reinforcement learning; Load balancing (electrical power); Enhanced Data Rates for GSM Evolution; Distributed computing; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001018035,0.0002698302,0.0002809332,0.0003509131,0.0005195013,0.0007171784,0.003042158,0.000108705,0.000002533664],"category_scores_gemma":[0.00003094575,0.0002905861,0.0001419082,0.001341304,0.00009478422,0.0003892535,0.002375849,0.0009952011,0.0001044289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004843116,"about_ca_system_score_gemma":0.0003145015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006549991,"about_ca_topic_score_gemma":0.00002676415,"domain_scores_codex":[0.9976629,0.0002957753,0.000620079,0.000540684,0.000291563,0.000588981],"domain_scores_gemma":[0.9967881,0.0008551438,0.0001128526,0.001999923,0.0001303766,0.0001135361],"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":[0.000002092497,0.00007934096,0.000886119,0.00006436821,0.0000311625,0.00002737229,0.002036577,0.7995374,0.00002000399,0.005053188,0.003016671,0.1892457],"study_design_scores_gemma":[0.0002515629,0.00005202626,0.00169173,0.0004893109,0.000006535753,0.00001453659,0.000008371795,0.9646617,0.000006209239,0.0002368838,0.03227652,0.000304583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001491647,0.002855863,0.9856879,0.002005545,0.004815588,0.0002473961,8.415761e-8,0.0009785253,0.001917467],"genre_scores_gemma":[0.9082297,0.00006351861,0.09053146,0.0004817393,0.0005120331,0.00001508642,0.00003270524,0.00002826481,0.0001055097],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.906738,"threshold_uncertainty_score":0.9999546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0169767421123599,"score_gpt":0.27428563428826,"score_spread":0.2573088921759001,"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."}}