{"id":"W4210701262","doi":"10.1109/globecom46510.2021.9685869","title":"Service Function Chain Reconfiguration in 5G Core Networks Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Global Communications Conference (GLOBECOM)","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Quality of service; Distributed computing; Software-defined networking; Virtual network; Computer network; Integer programming; Overhead (engineering); Service (business); Control reconfiguration; Mathematical optimization; Algorithm","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":[0.0005635713,0.0003055168,0.0003780389,0.0001000253,0.000598037,0.0005344596,0.001919216,0.0002607423,0.000156391],"category_scores_gemma":[0.0001544153,0.0003616007,0.0001072352,0.002770822,0.00007840115,0.0007791011,0.0006371339,0.0007330765,0.00008228975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003564403,"about_ca_system_score_gemma":0.000436167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00125831,"about_ca_topic_score_gemma":0.01395553,"domain_scores_codex":[0.9972118,0.0006351113,0.0006539691,0.0006527798,0.0002992884,0.0005470758],"domain_scores_gemma":[0.9960243,0.0003089488,0.0003095701,0.002406409,0.0007941225,0.000156709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004884031,0.0004932439,0.03055005,0.00004443413,0.0001247633,0.00004581863,0.0009124157,0.2478473,0.0007233708,0.1782864,0.0008896606,0.5400337],"study_design_scores_gemma":[0.0004692442,0.00004242438,0.01220154,0.0001654508,0.00002636826,0.00005343905,0.000470539,0.9772279,0.00002961393,0.004241304,0.004678273,0.0003939014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03340621,0.003134673,0.9512682,0.002736134,0.001152757,0.0003256312,0.000009271911,0.0003120084,0.007655124],"genre_scores_gemma":[0.9722766,0.001375358,0.02488022,0.001016363,0.0001200422,0.00004922901,0.0001868985,0.00001654926,0.00007873833],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9388704,"threshold_uncertainty_score":0.9998836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07806457376183402,"score_gpt":0.2961452317389593,"score_spread":0.2180806579771253,"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."}}