{"id":"W2121414586","doi":"10.1109/compsac.2013.21","title":"Cloud Client Prediction Models Using Machine Learning Techniques","year":2013,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Cloud computing; Support vector machine; Workload; Machine learning; Benchmark (surveying); Artificial intelligence; Virtual machine; Decision tree; Artificial neural network; Data mining; 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":[0.0002563738,0.0001114096,0.00009878056,0.0001019309,0.0002088695,0.0001998765,0.0004145849,0.00004149748,0.00002271045],"category_scores_gemma":[0.00000686565,0.00008911414,0.00005227665,0.0002153719,0.000017564,0.00008106941,0.0005392766,0.000154338,0.00003113583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005232436,"about_ca_system_score_gemma":0.000008657516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005589225,"about_ca_topic_score_gemma":0.000002234399,"domain_scores_codex":[0.9989753,0.00006076428,0.0001956348,0.0003029969,0.0002291374,0.0002361337],"domain_scores_gemma":[0.9994734,0.00002267183,0.00006455317,0.0003209282,0.00005282336,0.0000656482],"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":[0.000004144101,0.0002155032,0.002768349,0.00004221045,0.00006218048,0.00000938306,0.001439329,0.6578436,0.002848402,0.07468696,0.002387577,0.2576924],"study_design_scores_gemma":[0.00006578349,0.00007038876,0.00006490662,0.00001940577,0.000003323645,0.000008310464,0.00002642777,0.9920886,0.001384567,0.003255977,0.002907395,0.0001048853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1396384,0.00004753536,0.8498774,0.0003667043,0.0002073161,0.000200395,1.389377e-7,0.001071341,0.008590712],"genre_scores_gemma":[0.8690636,0.000005091623,0.1283207,0.000254861,0.000160149,0.00001194266,6.976111e-7,0.00001000263,0.002172982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7294252,"threshold_uncertainty_score":0.3633969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02069519917471621,"score_gpt":0.2256095275606504,"score_spread":0.2049143283859342,"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."}}