{"id":"W2292727630","doi":"10.14738/tmlai.41.1690","title":"Using Machine Learning Algorithms for Cloud Client Prediction Models in a Web VM Resource Provisioning Environment","year":2016,"lang":"en","type":"article","venue":"Transactions on Machine Learning and Artificial Intelligence","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Cloud computing; Computer science; Provisioning; Virtual machine; Machine learning; Support vector machine; Benchmark (surveying); Workload; Artificial intelligence; Service-level agreement; Data mining; Algorithm; Distributed computing; Operating 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009929886,0.0002712782,0.0002476981,0.0003410534,0.000815251,0.00015145,0.000304014,0.00009441705,0.00001898028],"category_scores_gemma":[0.00004395115,0.0002144664,0.0001135203,0.0003074475,0.00008988666,0.00009173826,0.00005654303,0.000536005,0.00001292272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001348028,"about_ca_system_score_gemma":0.00002457725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001315976,"about_ca_topic_score_gemma":0.00003157776,"domain_scores_codex":[0.9976225,0.0002643277,0.0005423173,0.0007720849,0.0003354858,0.0004633032],"domain_scores_gemma":[0.999045,0.0003788515,0.0001609663,0.0002620428,0.00002849611,0.0001247131],"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":[0.0000456141,0.0001010201,0.00009150806,0.00001168297,0.00001204308,0.000003112932,0.0006240743,0.5928486,0.0007991936,0.00134521,6.660227e-7,0.4041173],"study_design_scores_gemma":[0.0001956651,0.0004630806,0.00001129391,0.0001985848,0.00001821379,0.00001229415,0.0002255022,0.9902171,0.002043507,0.002181619,0.004177,0.000256079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05566692,0.0002025625,0.9423181,0.0008910791,0.0001816005,0.0003787588,0.000008656961,0.0002472446,0.000105052],"genre_scores_gemma":[0.9758298,0.0001200565,0.02307071,0.00004797252,0.0001050026,0.00003956365,0.000002971505,0.00003067195,0.0007532407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9201629,"threshold_uncertainty_score":0.8745685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04834691827674466,"score_gpt":0.2799360773037304,"score_spread":0.2315891590269857,"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."}}