{"id":"W2761012364","doi":"10.1109/icnets2.2017.8067887","title":"Token based energy aware scheduling algorithms for heterogeneous multi-core","year":2017,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Security token; Scheduling (production processes); Dynamic priority scheduling; Distributed computing; Fair-share scheduling; Round-robin scheduling; Fixed-priority pre-emptive scheduling; Two-level scheduling; Rate-monotonic scheduling; Efficient energy use; Multi-core processor; Energy consumption; Parallel computing; Algorithm; Mathematical optimization; Computer network; Quality of service; 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":[0.0001714734,0.0001379407,0.0001462647,0.00006920603,0.000636243,0.0004224412,0.00126501,0.00007728161,0.000005857183],"category_scores_gemma":[0.00004978286,0.0001251544,0.0001005854,0.00004449204,0.0000363781,0.0002407954,0.0002561923,0.00004879621,0.000004347124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002254168,"about_ca_system_score_gemma":0.00005849551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008847697,"about_ca_topic_score_gemma":0.00001469739,"domain_scores_codex":[0.9990416,0.00001656748,0.0001765997,0.0003790715,0.000126626,0.000259559],"domain_scores_gemma":[0.9986387,0.00005579602,0.0001425715,0.0009202049,0.0001548883,0.00008785318],"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":[0.00002732364,0.0004029848,0.001060535,0.00006456269,0.00008185057,0.00004967503,0.0002065315,0.5727306,0.0005113756,0.0256384,0.004115498,0.3951106],"study_design_scores_gemma":[0.0004220949,0.00005175976,0.00004256586,0.00001596427,0.000002343281,0.000004207347,0.000001237632,0.9807224,0.01581428,0.0005687689,0.00217475,0.0001796704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001909539,0.00003923416,0.9979021,0.0004677501,0.0002334911,0.0001166824,0.000002803733,0.0007044536,0.0003425611],"genre_scores_gemma":[0.3104114,0.000005037881,0.6885086,0.0005015086,0.00005587894,0.00002856809,0.000004815062,0.000009605301,0.000474557],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4079917,"threshold_uncertainty_score":0.510365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0791476886955363,"score_gpt":0.3303842499399506,"score_spread":0.2512365612444143,"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."}}