{"id":"W2253162084","doi":"","title":"Optimizing application downtime through intelligent VM placement and migration in cloud data centers","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Downtime; Computer science; Live migration; Cloud computing; Fault tolerance; Virtual machine; High availability; Control reconfiguration; Data center; Distributed computing; Reliability engineering; Operating system; Virtualization; Embedded system; Engineering","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":[0.0008507342,0.0008819783,0.0006513142,0.0004593657,0.0007051512,0.0008340827,0.001032935,0.000490918,0.0003245565],"category_scores_gemma":[0.002018359,0.0003661376,0.0002438187,0.000389318,0.0005355719,0.0006889501,0.0006209736,0.0005908332,0.0001242207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009228913,"about_ca_system_score_gemma":0.001414758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005595255,"about_ca_topic_score_gemma":0.007050738,"domain_scores_codex":[0.9994305,0.0001239399,0.00003036884,0.0001178339,0.0001266383,0.0001706664],"domain_scores_gemma":[0.999104,0.0003177099,0.0002179697,0.0001161306,0.0001183457,0.0001258436],"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":[0.0003236394,0.0001671623,0.003355058,0.00006091458,0.0000283524,0.00005969367,0.0001112189,0.8925381,0.04238028,0.0009969999,0.0004716213,0.05950687],"study_design_scores_gemma":[0.00001273608,0.0000864171,0.0006914202,0.000002847483,0.00001324632,0.00001958573,0.00004661279,0.9897156,0.008795369,0.0004205811,0.0001903982,0.000005224033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6314632,0.0006945807,0.3638319,0.0002401745,0.0000602382,0.0001189406,0.00005593167,0.001708858,0.001826215],"genre_scores_gemma":[0.9548351,0.0001020571,0.04464073,0.0000300156,0.000008230912,0.00001827663,0.00002531982,0.00004447268,0.000295858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005595255,"threshold_uncertainty_score":0.01112539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02672101578676284,"score_gpt":0.2411441765852903,"score_spread":0.2144231607985275,"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."}}