{"id":"W2024916734","doi":"10.1109/sis.2014.7011800","title":"A biogeography-based optimization algorithm for energy efficient virtual machine placement","year":2014,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Virtual machine; Energy consumption; Heuristic; Server; Genetic algorithm; Evolutionary algorithm; Virtualization; Mathematical optimization; Distributed computing; Cloud computing; Artificial intelligence; Machine learning; Engineering; Computer network; Operating system; Mathematics","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":[0.0006737477,0.0007870687,0.0009620814,0.001004801,0.0007044291,0.000826922,0.001017064,0.001550985,0.003029574],"category_scores_gemma":[0.001608797,0.0004895679,0.0007206801,0.001078806,0.0006743608,0.0007327764,0.0009222048,0.0007419254,0.0003934512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001200153,"about_ca_system_score_gemma":0.001648456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007422746,"about_ca_topic_score_gemma":0.006018225,"domain_scores_codex":[0.9997092,0.0001104778,0.00001404155,0.00005199139,0.00006978468,0.00004438586],"domain_scores_gemma":[0.9995912,0.0002345398,0.00004732219,0.00001789244,0.00007497294,0.00003414541],"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":[0.00002686277,0.0000258183,0.0005258419,0.00003444761,0.00002467244,0.0000405678,0.00002740566,0.9694659,0.000970873,0.003905943,0.0007631076,0.02418854],"study_design_scores_gemma":[0.00001723479,0.00001669356,0.0001038975,0.000005647728,0.000004589548,0.000014628,0.000007011766,0.997651,0.0001527252,0.001436275,0.0005870729,0.000003179824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02229966,0.0005382333,0.9701561,0.0004757601,0.0001103312,0.000109488,0.00009310112,0.0004162956,0.005800996],"genre_scores_gemma":[0.3609732,0.0005010713,0.6326055,0.0002959406,0.00005639444,0.0005201811,0.00027926,0.0001409484,0.004627481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007422746,"threshold_uncertainty_score":0.01475906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005244046078832609,"score_gpt":0.1983602953323221,"score_spread":0.1931162492534895,"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."}}