{"id":"W1076198238","doi":"10.4018/978-1-4666-4522-6.ch017","title":"Virtual Machine Migration in Cloud Computing Environments","year":2013,"lang":"en","type":"book-chapter","venue":"Advances in systems analysis, software engineering, and high performance computing book series","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Live migration; Cloud computing; Computer science; Virtualization; Virtual machine; Data center; Overhead (engineering); Data migration; Distributed computing; Computer network; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005872096,0.0007971211,0.00129469,0.001186096,0.0002570827,0.0003393046,0.0008210531,0.000288365,0.00001058196],"category_scores_gemma":[0.00002628446,0.0008069171,0.0001777266,0.0004283921,0.0001143636,0.0005023308,0.0007729197,0.0007096472,0.00002359957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002979249,"about_ca_system_score_gemma":0.00002971919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002215563,"about_ca_topic_score_gemma":0.00009511493,"domain_scores_codex":[0.9962817,0.00004569582,0.00128538,0.001127399,0.00059145,0.000668411],"domain_scores_gemma":[0.9981949,0.0002200739,0.0006346062,0.0007712778,0.00004710737,0.0001320139],"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":[0.000005753211,0.00002234647,0.00608481,0.0003509238,0.0002170412,0.00002641459,0.0006724008,0.9640863,0.000001762824,0.01392468,0.00003263729,0.01457493],"study_design_scores_gemma":[0.0003901299,0.0001548872,0.005016726,0.001192969,0.0001036058,0.00002231903,0.00003720654,0.8878616,0.000005994996,0.00004033806,0.1042498,0.0009243967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.129531,0.06924572,0.792923,0.00008905231,0.003269361,0.001301932,0.000017774,0.0009590305,0.002663152],"genre_scores_gemma":[0.9534836,0.00680953,0.008285946,0.00007009291,0.000588223,0.00002174491,0.00007977592,0.0001018647,0.0305592],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8239527,"threshold_uncertainty_score":0.9994382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003849529509780901,"score_gpt":0.1824207743692556,"score_spread":0.1785712448594746,"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."}}