{"id":"W4381415999","doi":"10.1109/tnsm.2023.3287757","title":"Cost-Efficient Cluster Migration of VNFs for Service Function Chain Embedding","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"","keywords":"Computer science; Scalability; Virtual network; Network Functions Virtualization; Distributed computing; Latency (audio); Embedding; Network virtualization; Computer network; Software deployment; Virtualization; Operating system; Cloud computing","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":[0.0004195837,0.001029286,0.0005860472,0.0005171615,0.001026935,0.0006426515,0.001144868,0.0006304047,0.002944067],"category_scores_gemma":[0.001371367,0.0002652477,0.0004758144,0.0006816019,0.0003530079,0.0008338543,0.0009320314,0.0006491006,0.0004477092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001307297,"about_ca_system_score_gemma":0.001720211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009314464,"about_ca_topic_score_gemma":0.01273041,"domain_scores_codex":[0.9996008,0.00008083486,0.00001748663,0.00007602045,0.00008709447,0.0001376646],"domain_scores_gemma":[0.9994876,0.0001637658,0.00006197542,0.000107852,0.0001016338,0.00007717418],"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":[0.0002176925,0.0001608747,0.001174809,0.0001082845,0.00002891201,0.0001347522,0.0001191573,0.848254,0.01223908,0.005513546,0.00530304,0.1267459],"study_design_scores_gemma":[0.00001285311,0.00003837055,0.0002199246,0.000004573348,0.000005739481,0.00003032137,0.00006026526,0.994782,0.002149506,0.001591884,0.001098567,0.000005946028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2264999,0.0005913667,0.7586768,0.000521309,0.0002239548,0.0003809378,0.0002659763,0.00323878,0.009600904],"genre_scores_gemma":[0.7359841,0.0001631893,0.2600521,0.00006130422,0.00002075817,0.0001498841,0.0004510861,0.0001481066,0.002969487],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009314464,"threshold_uncertainty_score":0.01852053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02952365512283914,"score_gpt":0.2584166368381917,"score_spread":0.2288929817153526,"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."}}