{"id":"W1912570954","doi":"10.1109/iccac.2015.29","title":"End-to-End QoS Prediction Model of Vertically Composed Cloud Services via Tensor Factorization","year":2015,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cloud computing; Quality of service; Computer science; Software as a service; Mobile QoS; The Internet; End user; Software; Service provider; Service (business); Web service; Throughput; Services computing; Computer network; World Wide Web; Software development; 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":[0.0009979194,0.001023886,0.0006751617,0.0006186618,0.0004886767,0.0007727057,0.001036828,0.0008507983,0.0009769519],"category_scores_gemma":[0.002055926,0.0004596195,0.0007136912,0.0006767981,0.0004825766,0.001049403,0.0004686728,0.001285823,0.0002524694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001580438,"about_ca_system_score_gemma":0.001543945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04973773,"about_ca_topic_score_gemma":0.02465712,"domain_scores_codex":[0.9995511,0.0000989992,0.00002923448,0.0001313286,0.00008865687,0.0001006558],"domain_scores_gemma":[0.9990808,0.0004080529,0.0001548738,0.00004813192,0.000243939,0.00006428036],"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":[0.00008019912,0.00003979337,0.001942297,0.00002152143,0.00002938945,0.00006066728,0.00003807175,0.9816959,0.001327334,0.001823283,0.0004097931,0.01253187],"study_design_scores_gemma":[6.017966e-7,0.000002431855,0.00004800604,4.410032e-7,0.000001224613,0.000001682525,0.000001061554,0.999709,0.00004735923,0.000176106,0.0000109869,0.000001060387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1383318,0.0003617974,0.8580738,0.0006777678,0.00006143228,0.00006267559,0.0003069634,0.0006048317,0.001518873],"genre_scores_gemma":[0.9393492,0.0003180069,0.05754367,0.00008595275,0.00003629448,0.00009256593,0.0004423627,0.00003335919,0.002098644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04973773,"threshold_uncertainty_score":0.09889644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02980051705070195,"score_gpt":0.2257213652159419,"score_spread":0.19592084816524,"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."}}