{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009644626,0.0008282543,0.0007343234,0.0008746973,0.0003740033,0.0008269323,0.0009022837,0.0007259159,0.001274999],"category_scores_gemma":[0.003638849,0.0003758278,0.0005212183,0.0009434363,0.0001889976,0.0009387275,0.0003043445,0.000965055,0.0005135676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001215358,"about_ca_system_score_gemma":0.0009075847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0312949,"about_ca_topic_score_gemma":0.01871385,"domain_scores_codex":[0.9994228,0.0001585201,0.00004315887,0.0001342123,0.0001591717,0.00008224414],"domain_scores_gemma":[0.9974483,0.001601122,0.0002154619,0.0001457231,0.0005389152,0.00005056489],"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":[0.00004048725,0.00005938257,0.00271598,0.00002059783,0.00002711303,0.00003670578,0.00001603613,0.9691588,0.0005689359,0.0004266401,0.0005666292,0.02636265],"study_design_scores_gemma":[5.973043e-7,0.000002596973,0.0001151748,7.785808e-7,0.000001098975,0.000002042596,0.00000121315,0.9996101,0.0001002368,0.0001371976,0.00002781715,0.000001078724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4038647,0.000848551,0.5830166,0.0008163023,0.0001110558,0.0001574874,0.0008611377,0.005262009,0.005062249],"genre_scores_gemma":[0.9527347,0.0002150867,0.04434971,0.00005586764,0.00004526095,0.00008978409,0.0004695981,0.00006118529,0.001978707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0312949,"threshold_uncertainty_score":0.06222552,"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."}}