{"id":"W2970072511","doi":"10.1109/cloud.2019.00023","title":"Cloud VM Provisioning Using Analytical Performance Models","year":2019,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Provisioning; Cloud computing; Computer science; Bottleneck; Virtual machine; Genetic algorithm; Distributed computing; Container (type theory); Workload; Minification; Computer network; Operating system; Engineering; Embedded system","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":[],"consensus_categories":[],"category_scores_codex":[0.0002963344,0.0001080459,0.0001291007,0.00008684147,0.0001180264,0.0001587309,0.0006420876,0.00003401284,0.00002214433],"category_scores_gemma":[0.000004225132,0.00008355901,0.0000570018,0.0003107157,0.0000156004,0.00007438178,0.0006773558,0.0001131429,0.000158405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004192911,"about_ca_system_score_gemma":0.00002822086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001917676,"about_ca_topic_score_gemma":2.612236e-7,"domain_scores_codex":[0.9988415,0.00002489726,0.0001762833,0.0003635043,0.0002972375,0.0002966012],"domain_scores_gemma":[0.9992772,0.00003281948,0.00004443204,0.0005405282,0.00003634617,0.00006865086],"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":[0.000003132089,0.0000310615,0.001010995,0.00002336749,0.0000131327,0.000003462445,0.000227632,0.8974135,0.00003566825,0.07737099,0.0002033586,0.02366372],"study_design_scores_gemma":[0.0001527604,0.00006286014,0.0002007537,0.00003591591,0.000003511888,0.000007987725,0.00002646132,0.9979197,0.00007128639,0.0005092474,0.0008701879,0.0001393448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6964839,0.00001451034,0.2760922,0.0001740494,0.0002714465,0.00009901276,3.103278e-8,0.0001648887,0.02669995],"genre_scores_gemma":[0.9712847,7.497059e-7,0.02430984,0.0002647877,0.00008400799,7.360646e-7,1.035327e-7,0.000006652471,0.004048395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2748008,"threshold_uncertainty_score":0.3407438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0258370183905096,"score_gpt":0.2419279519686328,"score_spread":0.2160909335781231,"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."}}