{"id":"W2523978226","doi":"10.1109/tcc.2015.2440246","title":"Off-Peak Energy Optimization for Links in Virtualized Network Environment","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Cloud Computing","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Energy consumption; Control reconfiguration; Scalability; Distributed computing; Heuristic; Network virtualization; Virtualization; Computer network; Virtual network; Population; Integer programming; Efficient energy use; Cloud computing; Engineering; Embedded system","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":[0.0004130137,0.0008502136,0.0006675451,0.0004760342,0.0005219267,0.001158385,0.0006920755,0.0005901063,0.002301322],"category_scores_gemma":[0.0008005488,0.0003330879,0.0004330816,0.0006506469,0.000480667,0.0008890434,0.0006329984,0.0005694401,0.0001290897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001146212,"about_ca_system_score_gemma":0.0008941734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005555273,"about_ca_topic_score_gemma":0.006575622,"domain_scores_codex":[0.9997009,0.00009185333,0.000005532633,0.00004834476,0.00005008727,0.000103388],"domain_scores_gemma":[0.9997243,0.0001628757,0.00003657231,0.00001162295,0.00003760585,0.0000268566],"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":[0.00003091314,0.00001729672,0.0001262286,0.00001875621,0.000006261129,0.00002619561,0.00001143853,0.9932619,0.0005971842,0.001793367,0.0002317343,0.003878789],"study_design_scores_gemma":[0.000003248836,0.00001738137,0.00005744514,0.000001679612,0.000002794735,0.00000581435,0.00001507181,0.9984257,0.0001878678,0.001154926,0.0001264573,0.000001614859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2373028,0.001126033,0.7424604,0.000584476,0.00009286925,0.0001152039,0.0002389794,0.0003695727,0.01770984],"genre_scores_gemma":[0.9784303,0.0002409948,0.0183541,0.00005436718,0.00001187775,0.00005146301,0.00005882515,0.00004948182,0.002748586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005555273,"threshold_uncertainty_score":0.01104587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02680151553321254,"score_gpt":0.2390261424410535,"score_spread":0.2122246269078409,"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."}}