{"id":"W1530474892","doi":"10.1109/icc.2015.7249198","title":"IoNCloud: Exploring application affinity to improve utilization and predictability in datacenters","year":2015,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Predictability; Overhead (engineering); Cloud computing; Bandwidth (computing); Resource management (computing); Resource allocation; Shared resource; Distributed computing; Abstraction; Computer network; Scheme (mathematics); 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":[],"consensus_categories":[],"category_scores_codex":[0.0005636273,0.00007176506,0.00007585668,0.00007720126,0.00003740063,0.00007652867,0.0003135335,0.00001860708,4.891911e-7],"category_scores_gemma":[0.00006750685,0.00006482015,0.000009567395,0.0003234459,0.00001250853,0.00007076393,0.0006728309,0.00005137677,0.000009345636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009565178,"about_ca_system_score_gemma":0.00001517002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002262333,"about_ca_topic_score_gemma":0.00006340654,"domain_scores_codex":[0.9991158,0.00004150008,0.000155742,0.0003752082,0.0001665736,0.0001451615],"domain_scores_gemma":[0.9993438,0.000027974,0.00002935892,0.0004392804,0.00003472737,0.0001248915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000315071,0.000310464,0.08021768,0.00006141489,0.00001064292,0.000003098611,0.007860431,0.01171609,0.00035845,0.04553571,0.0009661485,0.8529283],"study_design_scores_gemma":[0.0005756811,0.0001079528,0.0664874,0.00001875714,0.000002504773,9.830114e-7,0.0006004,0.9184608,0.0005402215,0.0009750529,0.01205268,0.0001775705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6149488,0.00001386503,0.3830698,0.0007063458,0.0001376797,0.0002301647,4.476565e-7,0.0001162661,0.0007766179],"genre_scores_gemma":[0.9906923,0.000002041086,0.009026258,0.0001608357,0.00003400645,0.00004256515,0.000001704238,0.0000031286,0.00003717632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9067447,"threshold_uncertainty_score":0.2643289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0979720365755188,"score_gpt":0.2794101268368863,"score_spread":0.1814380902613675,"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."}}