{"id":"W1989673248","doi":"10.1109/ccece.2013.6567848","title":"Predicting cloud resource provisioning using machine learning techniques","year":2013,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Provisioning; Computer science; Cloud computing; Support vector machine; Machine learning; Benchmark (surveying); Virtual machine; Service-level agreement; Artificial intelligence; Resource (disambiguation); Artificial neural network; Data mining; Throughput; Distributed computing; Operating system; Computer network","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":[0.001032777,0.0006029714,0.0005472184,0.001116017,0.0002713794,0.0006296622,0.0004790718,0.0006038763,0.0006394854],"category_scores_gemma":[0.004535791,0.0002778582,0.0003436212,0.000902423,0.0001577783,0.0008120752,0.0002272567,0.0006354331,0.0002671971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008803319,"about_ca_system_score_gemma":0.0007136719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01514347,"about_ca_topic_score_gemma":0.0110068,"domain_scores_codex":[0.9995314,0.0001402233,0.00003802982,0.00008998204,0.0001304918,0.00006993782],"domain_scores_gemma":[0.9971366,0.001964508,0.0002438165,0.0001433572,0.000441012,0.0000707197],"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":[0.00007370856,0.0001499622,0.01310784,0.00002863894,0.00003482135,0.00004921155,0.00001597461,0.9390959,0.001797077,0.0002457681,0.0005827073,0.04481834],"study_design_scores_gemma":[8.607745e-7,0.000006968721,0.0006197251,9.960792e-7,0.000001407267,0.000002191656,0.000002552476,0.9989724,0.0002771009,0.00008919793,0.00002518781,0.000001394199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8483188,0.0004737865,0.146028,0.0005459588,0.00006414361,0.00008919874,0.0004825995,0.002054113,0.00194343],"genre_scores_gemma":[0.9816608,0.00009810682,0.01760021,0.00002467969,0.00001485227,0.00002528107,0.0002527694,0.0000124381,0.0003106523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01514347,"threshold_uncertainty_score":0.0301106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01245012065963091,"score_gpt":0.2300612838209695,"score_spread":0.2176111631613386,"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."}}