{"id":"W3001462167","doi":"10.1109/tcomm.2020.2968907","title":"Dynamic Flow Migration for Embedded Services in SDN/NFV-Enabled 5G Core Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Computer science; Quality of service; Software-defined networking; Integer programming; Distributed computing; Multi-commodity flow problem; Control reconfiguration; Computer network; Heuristic; Provisioning; Mathematical optimization; Flow network; Algorithm","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":[0.000708835,0.0006678143,0.0004670891,0.0003300546,0.0005816851,0.0006438976,0.0006158861,0.0006479662,0.001022216],"category_scores_gemma":[0.001006772,0.0002317313,0.0003259413,0.0004030404,0.000420484,0.0007768128,0.0006369581,0.0005029488,0.00007781165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001230109,"about_ca_system_score_gemma":0.0009199038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005768043,"about_ca_topic_score_gemma":0.006106261,"domain_scores_codex":[0.9997249,0.00009532765,0.000008499207,0.00005139717,0.00003640731,0.00008353259],"domain_scores_gemma":[0.9997401,0.0001339999,0.00003946737,0.00001606791,0.00003813078,0.00003229021],"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":[0.00007511519,0.00005661244,0.0004892402,0.00003254748,0.000008665668,0.00008620964,0.00003565721,0.9622557,0.003097788,0.004918968,0.000641548,0.02830207],"study_design_scores_gemma":[0.000002681219,0.00001469734,0.00005543001,0.000001856663,0.000001685516,0.000008762418,0.00001441934,0.9981173,0.0003325132,0.00122915,0.0002197866,0.000001758076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2120295,0.0007575987,0.7813293,0.0006031178,0.0001202392,0.0001073753,0.0000721522,0.0002553923,0.004725323],"genre_scores_gemma":[0.9283623,0.000233384,0.06980574,0.00007513575,0.00002097621,0.00004972217,0.00007342835,0.00002758657,0.001351673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005768043,"threshold_uncertainty_score":0.01146889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03483660970441744,"score_gpt":0.2732167042613475,"score_spread":0.2383800945569301,"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."}}