{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001218001,0.0001426098,0.0001325047,0.0001768014,0.0001149498,0.0003451617,0.001013598,0.00002838085,1.353948e-7],"category_scores_gemma":[0.00007465023,0.0001351729,0.00001059406,0.0006052804,0.00006990009,0.0003019735,0.002013286,0.0001061594,0.000002837226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001015287,"about_ca_system_score_gemma":0.00004892724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005461218,"about_ca_topic_score_gemma":0.000005365,"domain_scores_codex":[0.9984275,0.00001498609,0.0002138466,0.0006628454,0.000380901,0.0002999219],"domain_scores_gemma":[0.9990484,0.00006307252,0.00004973179,0.0006364665,0.00006779325,0.0001345016],"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":[0.000003425081,0.00005122179,0.002339955,0.00004852727,0.000009779299,0.0000107424,0.008366203,0.8293059,0.00008263263,0.002357242,0.0004293256,0.156995],"study_design_scores_gemma":[0.0001904306,0.00003699118,0.0006420934,0.00005910425,0.000002382637,0.00001160589,0.00005840902,0.9957054,0.00009072652,0.00006907642,0.002971051,0.0001627137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1214494,0.0003335324,0.8772002,0.0002536905,0.0004267067,0.000164309,5.059755e-7,0.0001617054,0.000009887393],"genre_scores_gemma":[0.5469725,0.00002727053,0.4526749,0.0001854644,0.0001149675,0.000007958641,0.000003825152,0.000006859168,0.00000632002],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4255231,"threshold_uncertainty_score":0.5512193,"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."}}