{"id":"W1589014581","doi":"10.1109/inm.2015.7140375","title":"ViNO: SDN overlay to allow seamless migration across heterogeneous infrastructure","year":2015,"lang":"en","type":"article","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Overlay; Computer science; Network topology; Overlay network; Orchestration; Virtual network; Downtime; Computer network; Network virtualization; Distributed computing; Virtual machine; Domain (mathematical analysis); Virtualization; Operating system; Cloud computing; The Internet","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":[0.0001840525,0.0001743996,0.0001614025,0.00003969634,0.00009771934,0.0003442874,0.0007152869,0.0001061391,0.00002434404],"category_scores_gemma":[0.00004599679,0.0001420603,0.00006027989,0.0003893367,0.00001763163,0.0003372106,0.0003731521,0.000105301,0.0001667144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007108078,"about_ca_system_score_gemma":0.00006698556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001412011,"about_ca_topic_score_gemma":0.0004609202,"domain_scores_codex":[0.9985849,0.00003689091,0.0001893139,0.0004210777,0.0003589041,0.0004089025],"domain_scores_gemma":[0.9988343,0.00004561647,0.00004801661,0.0005959406,0.0001439911,0.0003321767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009650663,0.0001632956,0.04192253,0.00002481475,0.00009283858,0.0001534333,0.01301433,0.2463831,0.001090308,0.01307518,0.435842,0.2481417],"study_design_scores_gemma":[0.002640468,0.001235508,0.05910793,0.00007238403,0.00001952848,0.0003997193,0.0003159469,0.4102925,0.01320279,0.01453154,0.4960367,0.002144975],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4099028,0.00009294549,0.5865559,0.001443419,0.0008699197,0.0001650626,0.000004789185,0.0003903005,0.0005749112],"genre_scores_gemma":[0.9577361,0.000005275317,0.03801075,0.003314616,0.0002220315,0.00001553184,0.000006697232,0.00001402647,0.000674965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5485452,"threshold_uncertainty_score":0.5793052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01897704267444538,"score_gpt":0.2677095861996343,"score_spread":0.2487325435251889,"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."}}