{"id":"W2123590764","doi":"10.1186/s13677-014-0023-3","title":"Fine-grained preemption analysis for latency investigation across virtual machines","year":2014,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Preemption; Virtual machine; Hypervisor; Operating system; Thread (computing); Latency (audio); Tracing; Interrupt; Real-time computing; Cloud computing; Embedded system; Virtualization","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":[0.001336173,0.0001509828,0.0003689806,0.0001719488,0.0004443368,0.0002540524,0.0004947964,0.00004839587,2.070064e-7],"category_scores_gemma":[0.00006065382,0.000122258,0.0001676554,0.0006939168,0.00005208267,0.00007606691,0.0001365318,0.0001211938,0.000001046681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002899406,"about_ca_system_score_gemma":0.00001798344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001619489,"about_ca_topic_score_gemma":0.000008755184,"domain_scores_codex":[0.9984184,0.0001071282,0.0007058021,0.0002880002,0.0002611116,0.0002195814],"domain_scores_gemma":[0.9979317,0.0004008048,0.0009481037,0.0003073056,0.000298035,0.0001140278],"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":[0.000008921251,0.00005734921,0.008609291,0.0001327578,0.0002203429,4.802657e-7,0.0008861567,0.7143269,0.0001560637,0.0764087,0.0001411881,0.1990519],"study_design_scores_gemma":[0.0004844145,0.0002050088,0.005513245,0.00008250498,0.00009761588,0.00002577577,0.0001214207,0.9697767,0.00002320994,0.004388321,0.01911256,0.0001691704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2548539,0.0006542007,0.7434558,0.0003900769,0.0003042104,0.0002256389,0.00000238396,0.00005234799,0.00006148775],"genre_scores_gemma":[0.9819289,0.00001091148,0.01676278,0.00004689033,0.001090032,0.0000187538,0.00000331142,0.000008303347,0.0001300471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7270751,"threshold_uncertainty_score":0.4985537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01297519960267469,"score_gpt":0.2741907566374286,"score_spread":0.2612155570347539,"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."}}