{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006641564,0.0008909459,0.0008163,0.0003781444,0.0004802723,0.0006948188,0.001029065,0.001009448,0.0012852],"category_scores_gemma":[0.001545402,0.0004970895,0.0004490564,0.0004450633,0.0006695489,0.001000185,0.0008739533,0.001261069,0.0001369952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001981578,"about_ca_system_score_gemma":0.001745342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01766915,"about_ca_topic_score_gemma":0.02029356,"domain_scores_codex":[0.9997019,0.00007035095,0.00001230186,0.00007348233,0.00005301604,0.00008889912],"domain_scores_gemma":[0.999456,0.0003220608,0.00006975769,0.00002460181,0.00008464792,0.00004307881],"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":[0.00003341534,0.00003229984,0.0003555343,0.00001686419,0.000009166016,0.00002207273,0.0000157999,0.9753495,0.000500124,0.001077459,0.0002958306,0.02229194],"study_design_scores_gemma":[0.000001373094,0.000004363193,0.00001783684,7.741094e-7,8.352997e-7,0.000001424044,0.000001714543,0.9994276,0.00008341535,0.0004333185,0.00002664917,6.407458e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1214898,0.0007379801,0.87251,0.0005807362,0.00005942412,0.00007542653,0.00006993012,0.0007736776,0.003703076],"genre_scores_gemma":[0.9352381,0.000183974,0.06235195,0.0001599042,0.00002680197,0.00007342041,0.0001161186,0.00004151867,0.001808249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01766915,"threshold_uncertainty_score":0.03513259,"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."}}