{"id":"W3001625778","doi":"10.1007/s12652-019-01630-6","title":"Efficient algorithms to minimize the end-to-end latency of edge network function virtualization","year":2020,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Latency (audio); Network virtualization; Integer programming; Algorithm; Virtual network; Time complexity; Virtualization; Enhanced Data Rates for GSM Evolution; Edge computing; Distributed computing; Mathematical optimization; Cloud computing; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0009236343,0.0001705477,0.0003428186,0.0001208408,0.0002885084,0.0001752971,0.0006949127,0.0000495877,0.0000232583],"category_scores_gemma":[0.000148224,0.0001237069,0.0001231402,0.0008920367,0.00004642221,0.0001026143,0.0003732787,0.0002418071,0.00001207743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003063927,"about_ca_system_score_gemma":0.00005452209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001124322,"about_ca_topic_score_gemma":0.000001283922,"domain_scores_codex":[0.9979819,0.0001102639,0.0008415423,0.0002832563,0.0004840628,0.0002989566],"domain_scores_gemma":[0.998293,0.0003607226,0.0005298163,0.0002094936,0.0003881807,0.0002187577],"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":[0.0001294948,0.00008248213,0.0005840177,0.00002264745,0.00006450564,0.00001386092,0.009370586,0.736478,0.0003125647,0.0160054,0.001128208,0.2358083],"study_design_scores_gemma":[0.0003655181,0.001475684,0.0041197,0.0003552379,0.00006261031,0.00005445676,0.0006826671,0.9869137,0.001107013,0.001873668,0.002697077,0.0002926489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06579618,0.0005720434,0.931254,0.001116952,0.0009970469,0.0001785666,6.344989e-7,0.00003266312,0.0000518557],"genre_scores_gemma":[0.9479834,0.00004684253,0.04892815,0.00222234,0.0007955041,0.000001181152,6.561152e-7,0.00001184434,0.0000100135],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8823259,"threshold_uncertainty_score":0.5044622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03810651614543356,"score_gpt":0.2585823314033734,"score_spread":0.2204758152579399,"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."}}