{"id":"W2107463456","doi":"10.1109/cec.2006.1688723","title":"An Efficient Genetic Algorithm for Task Scheduling in Heterogeneous Distributed Computing Systems","year":2006,"lang":"en","type":"article","venue":"","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Fair-share scheduling; Distributed computing; Scheduling (production processes); Dynamic priority scheduling; Two-level scheduling; Rate-monotonic scheduling; Parallel computing; Fixed-priority pre-emptive scheduling; Speedup; Symmetric multiprocessor system; Schedule; Mathematical optimization; 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":[0.0007702576,0.0007194037,0.000638574,0.0007118976,0.0006065313,0.0005411982,0.000957918,0.0009549228,0.0008514796],"category_scores_gemma":[0.001957932,0.0003243805,0.0004880104,0.0008734917,0.0005317972,0.0005987624,0.000574096,0.0006619201,0.0001968396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007423433,"about_ca_system_score_gemma":0.001267208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005567743,"about_ca_topic_score_gemma":0.004601726,"domain_scores_codex":[0.999689,0.0001019865,0.00001205144,0.00005050402,0.0001108828,0.00003567568],"domain_scores_gemma":[0.9997057,0.0001676387,0.0000256946,0.00002380345,0.00006106085,0.00001602803],"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":[0.00004115424,0.00005021725,0.0003021766,0.00002864547,0.00002871987,0.00006302945,0.00004260863,0.922693,0.003257729,0.007716522,0.000821883,0.06495434],"study_design_scores_gemma":[0.0000244773,0.00002064359,0.00006733362,0.000002540657,0.000006490022,0.00001369615,0.000005429768,0.9960931,0.0004466207,0.002697338,0.0006181258,0.000004251384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01754632,0.0001667146,0.9803366,0.00009886257,0.00004122129,0.00006914111,0.00002488238,0.0003279342,0.001388332],"genre_scores_gemma":[0.2393728,0.0002722414,0.7574593,0.0001082412,0.00004576992,0.0003442497,0.000178989,0.0000881041,0.002130292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005567743,"threshold_uncertainty_score":0.01107067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0099877368913696,"score_gpt":0.2411575414989074,"score_spread":0.2311698046075378,"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."}}