{"id":"W4312295420","doi":"10.1109/tnsm.2022.3217723","title":"Reinforcement Learning-Based Optimization Framework for Application Component Migration in NFV Cloud-Fog Environments","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Concordia University","keywords":"Computer science; Cloud computing; Virtual network; Reinforcement learning; Distributed computing; Markov decision process; Component (thermodynamics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003864483,0.0001595714,0.0001339296,0.0001513819,0.0007357808,0.0000780787,0.0002928567,0.00004316528,0.000008477859],"category_scores_gemma":[4.464897e-7,0.0001880707,0.00004905974,0.0005406633,0.000009018349,0.0001113069,0.000020992,0.0002289289,0.00000553448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001786947,"about_ca_system_score_gemma":0.00001115595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004173139,"about_ca_topic_score_gemma":0.00001495357,"domain_scores_codex":[0.9986207,0.00009702973,0.0002956244,0.0004145492,0.0002804603,0.0002915709],"domain_scores_gemma":[0.9994268,0.00009179738,0.0001209711,0.0002945297,0.00001431193,0.00005153033],"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":[0.00005504305,0.0001288335,0.00002487259,0.00004184007,0.00002115096,0.000001043023,0.0003344126,0.9743805,0.000005025649,0.0008405541,0.0001434344,0.02402329],"study_design_scores_gemma":[0.0006361244,0.0001637851,0.0001398098,0.00002954685,0.00002175832,5.989473e-7,0.00006526068,0.9735363,0.00004009156,0.0004673324,0.02471731,0.0001820907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001402859,0.00002449442,0.9938915,0.001656134,0.001861861,0.0009450193,3.859465e-7,0.00008598635,0.0001317165],"genre_scores_gemma":[0.9006184,0.000125774,0.09456731,0.00286655,0.0003264573,0.001241036,0.00006765543,0.00002780204,0.0001590668],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8993242,"threshold_uncertainty_score":0.7669299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01005818046399027,"score_gpt":0.2130066221827625,"score_spread":0.2029484417187722,"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."}}