{"id":"W2146212558","doi":"10.1145/2479871.2479908","title":"Towards building performance models for data-intensive workloads in public clouds","year":2013,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"European Commission; Comunidad de Madrid","keywords":"Cloud computing; Computer science; Workload; Provisioning; Data modeling; Classifier (UML); Data mining; Performance prediction; Machine learning; Distributed computing; Artificial intelligence; Database; Simulation; 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":[0.002294647,0.001348554,0.0008826804,0.001131456,0.0004750071,0.001509393,0.001435542,0.00123105,0.0007287959],"category_scores_gemma":[0.01224837,0.000818603,0.00092168,0.001018469,0.0004568458,0.00220759,0.0005481315,0.001928752,0.0005903001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00194038,"about_ca_system_score_gemma":0.002044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01862922,"about_ca_topic_score_gemma":0.01457304,"domain_scores_codex":[0.9989743,0.000378044,0.00006267968,0.0001715036,0.0002671551,0.0001464484],"domain_scores_gemma":[0.9934715,0.004031519,0.0007834038,0.0004521206,0.001150238,0.0001112313],"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":[0.00003582364,0.0001060666,0.003424677,0.00003435168,0.00002677823,0.00002200714,0.00005913319,0.981926,0.001237229,0.001839659,0.0004435795,0.0108448],"study_design_scores_gemma":[0.000001231365,0.000007498093,0.0003284733,0.000002881079,0.000002399004,0.000003135525,0.000008447595,0.9985225,0.000280238,0.0007393647,0.0001009749,0.000002856162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1967757,0.0003290377,0.7953524,0.0007971288,0.00004058103,0.0002462358,0.0007695927,0.002991793,0.002697516],"genre_scores_gemma":[0.8823534,0.0002936587,0.1143585,0.0001124521,0.00005298314,0.0003006527,0.001045617,0.0001981485,0.001284603],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01862922,"threshold_uncertainty_score":0.0370416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06812584661816481,"score_gpt":0.2684938959340038,"score_spread":0.200368049315839,"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."}}