{"id":"W2028281082","doi":"10.1109/smc.2013.702","title":"Energy-Efficient Virtual Machine Management in Heterogeneous Environment: Challenges, Approaches and Opportunities","year":2013,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Virtualization; Cloud computing; Computer science; Efficient energy use; Virtual machine; Data center; Key (lock); Energy management; Data science; Scale (ratio); Distributed computing; Energy (signal processing); Risk analysis (engineering); Computer security; Business; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001447382,0.0004092072,0.0006844251,0.0005582774,0.000700089,0.002562982,0.001594897,0.001060016,0.0009532674],"category_scores_gemma":[0.001460513,0.0002203507,0.0002680335,0.001134192,0.000949402,0.004137169,0.001531512,0.001002192,0.0002067258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005903626,"about_ca_system_score_gemma":0.0006737101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005931462,"about_ca_topic_score_gemma":0.0007472411,"domain_scores_codex":[0.99936,0.000200859,0.00002792943,0.00009120195,0.0002104605,0.0001095651],"domain_scores_gemma":[0.9989688,0.00054063,0.00009302074,0.0001079141,0.0001925902,0.00009711503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001360228,0.0002493191,0.003475389,0.0009386396,0.0001074177,0.0003113248,0.0003922064,0.1684391,0.008217815,0.2686659,0.009947774,0.5391191],"study_design_scores_gemma":[0.00003757485,0.0001907866,0.001682704,0.0002715773,0.00005181858,0.0004959613,0.001403986,0.6323296,0.005936437,0.2934053,0.0641202,0.00007412745],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0918907,0.1618786,0.6904252,0.02600388,0.0007937447,0.000105734,0.00008048119,0.0004140757,0.02840753],"genre_scores_gemma":[0.8591929,0.04472515,0.09110799,0.0006313827,0.0009955264,0.00008181876,0.00006043977,0.00006651691,0.003138239],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.002562982,"threshold_uncertainty_score":0.007654548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04664228991628032,"score_gpt":0.1888741091497128,"score_spread":0.1422318192334325,"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."}}