{"id":"W3080073809","doi":"10.1109/tccn.2020.3018157","title":"Dynamic Resource Scaling for VNF Over Nonstationary Traffic: A Learning Approach","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Provisioning; Quality of service; Resource allocation; Scalability; Computer network; Software-defined networking; Markov decision process; Markov process; Traffic generation model; Distributed 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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0002655918,0.0002032571,0.000215665,0.0001081402,0.001325647,0.0001855586,0.0005809593,0.00009560175,0.000004208369],"category_scores_gemma":[0.00001319307,0.0002174535,0.0001243468,0.0006371532,0.0001366689,0.0002553471,0.0000251644,0.0005429653,0.000004188074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003042477,"about_ca_system_score_gemma":0.00004275847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003651363,"about_ca_topic_score_gemma":0.000008089978,"domain_scores_codex":[0.9985906,0.000200116,0.0002952113,0.0004578451,0.000156916,0.0002992725],"domain_scores_gemma":[0.997476,0.001734069,0.0001225265,0.0004047317,0.0001180057,0.0001446003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007729285,0.0001605484,0.00002890422,0.00003005979,0.00008986203,0.000001011606,0.002388145,0.06859661,0.0000217671,0.0004457318,0.00008743054,0.9280726],"study_design_scores_gemma":[0.0007468613,0.0001636524,0.00005431371,0.0001512827,0.00005493233,0.000009222003,0.0004256998,0.9898503,0.00001241932,0.0001137393,0.008161422,0.000256159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002304689,0.00183611,0.9932387,0.001080387,0.0001001924,0.0004566679,0.00001912544,0.0003270025,0.0006371472],"genre_scores_gemma":[0.9382092,0.001484345,0.05872964,0.001186369,0.00006374834,0.0002144267,0.00004357231,0.00003050451,0.00003818265],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9359045,"threshold_uncertainty_score":0.9999745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04431378056773156,"score_gpt":0.2776108599146176,"score_spread":0.2332970793468861,"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."}}