{"id":"W2279944032","doi":"10.1109/cloudcom.2015.45","title":"A Dynamic Bandwidth Prediction and Provisioning Scheme in Cloud Networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Provisioning; Cloud computing; Autoregressive integrated moving average; Quality of service; Crowds; Real-time computing; Distributed computing; Computer network; Time series; Machine learning","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":[0.001061988,0.0005252881,0.0006193282,0.0005077931,0.0007135658,0.0008726502,0.001322134,0.0005340979,0.0006323393],"category_scores_gemma":[0.002862665,0.0002306162,0.0002632097,0.0006358057,0.000446709,0.001019321,0.0006878253,0.0008416489,0.0001983593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001326252,"about_ca_system_score_gemma":0.001690284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02719713,"about_ca_topic_score_gemma":0.01611462,"domain_scores_codex":[0.9995097,0.00008878129,0.00002279944,0.0001365439,0.0001336224,0.0001086264],"domain_scores_gemma":[0.9992232,0.0002588704,0.00009600164,0.0001480048,0.0001979936,0.00007586247],"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":[0.0001374843,0.00007574306,0.002258544,0.00002261595,0.00001514546,0.00007150578,0.00006558405,0.9038292,0.006472797,0.005841492,0.001033679,0.08017617],"study_design_scores_gemma":[9.825184e-7,0.00000560597,0.0001053955,0.000001102282,0.00000130888,0.000004553208,0.000004856693,0.9987956,0.0005067143,0.0004885666,0.00008324518,0.0000019971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1470496,0.0002496394,0.8487837,0.0002998658,0.00007531855,0.00007554133,0.0001495826,0.001352389,0.001964469],"genre_scores_gemma":[0.938358,0.00008108273,0.06080904,0.00003573884,0.00002071696,0.00002919471,0.00008044729,0.00002918397,0.0005566388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02719713,"threshold_uncertainty_score":0.05407763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01050396426778555,"score_gpt":0.2210917619463231,"score_spread":0.2105877976785376,"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."}}