{"id":"W4248510974","doi":"10.32920/ryerson.14651676","title":"The effects of a fault management architecture on the performance of a cloud based application","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Fault management; Computer science; Scalability; Workload; Throughput; Fault (geology); Cloud computing; Architecture; Distributed computing; Fault detection and isolation; Set (abstract data type); Fault coverage; Reliability engineering; Real-time computing; Engineering; Database; Operating system; Artificial intelligence","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":[0.000816555,0.0001963582,0.0002628526,0.00005518286,0.0001373291,0.0000618418,0.001749604,0.0001210937,0.000002353378],"category_scores_gemma":[0.00004330391,0.00009059678,0.0001868056,0.0002997292,0.0001066044,0.00003103977,0.0008816829,0.0003635794,0.000004856657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004144679,"about_ca_system_score_gemma":0.00009923663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003203699,"about_ca_topic_score_gemma":0.000006701413,"domain_scores_codex":[0.9982924,0.0001806765,0.0003935245,0.0004287087,0.0005251969,0.0001795247],"domain_scores_gemma":[0.9963124,0.0007794319,0.0003327254,0.002399834,0.0001489416,0.00002666407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002157077,0.001050794,0.007616731,0.04341311,0.0008041805,0.000008121444,0.004816407,0.1112085,0.001606388,0.04583517,0.0021705,0.7812544],"study_design_scores_gemma":[0.001127775,0.0008098993,0.04116483,0.005050341,0.0001296826,0.000006034983,0.0002485649,0.7727948,0.1703297,0.003211877,0.004296745,0.0008296515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6470443,0.0002641477,0.3478919,0.001081748,0.0008962755,0.001756477,0.000001454013,0.00008016752,0.0009835036],"genre_scores_gemma":[0.9950292,0.0001544211,0.004050522,0.0001828804,0.00004579421,0.0004176033,0.00000370926,0.000007135533,0.0001087346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7804247,"threshold_uncertainty_score":0.3694429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004407661013074601,"score_gpt":0.2115999482287114,"score_spread":0.2071922872156368,"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."}}