{"id":"W2525648680","doi":"10.1109/tnsm.2016.2574239","title":"Dedicated Protection for Survivable Virtual Network Embedding","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); Cisco Systems (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Backup; Computer science; Provisioning; Drone; Network virtualization; Computer network; Distributed computing; Virtualization; Virtual network; Embedding; Heuristic; Service provider; Service (business); Operating system; Cloud computing","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":[0.000630882,0.0008219465,0.0004778789,0.0003365112,0.0003941557,0.0007091394,0.0009018935,0.0007120119,0.001394951],"category_scores_gemma":[0.001952329,0.0002589851,0.0004842445,0.0003581306,0.000688763,0.001515426,0.001264986,0.0008790173,0.0001606175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007117782,"about_ca_system_score_gemma":0.0007650764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008153298,"about_ca_topic_score_gemma":0.001154811,"domain_scores_codex":[0.9994668,0.0001963817,0.00002194484,0.0000972593,0.0001020134,0.0001156735],"domain_scores_gemma":[0.9992409,0.0003460614,0.000115743,0.0001619273,0.00007615413,0.00005924123],"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":[0.0001309521,0.00008561846,0.0005032124,0.0001534278,0.00004351016,0.0001659118,0.000110894,0.8238452,0.01238028,0.05423727,0.00273543,0.1056083],"study_design_scores_gemma":[0.000008911358,0.00006704912,0.0001170548,0.00001563568,0.00001250039,0.0001218131,0.00003589357,0.9725977,0.003075196,0.02094887,0.00299088,0.000008647941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04198424,0.0007140003,0.9525346,0.0002946044,0.00008022651,0.00005713035,0.00005298922,0.0002886479,0.003993479],"genre_scores_gemma":[0.8500783,0.0004965264,0.1467227,0.00009974303,0.00004435015,0.00006449696,0.0001076376,0.00006344188,0.002322791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001394951,"threshold_uncertainty_score":0.005164385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01893925904148043,"score_gpt":0.2270963733186233,"score_spread":0.2081571142771429,"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."}}