{"id":"W7125601066","doi":"10.1109/cascon66301.2025.00060","title":"AttentiveDRL: Fair and Efficient GPU Job Scheduling via Dual-Agent RL","year":2025,"lang":"","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); York University","funders":"","keywords":"Scheduling (production processes); Reinforcement learning; Job scheduler; Job shop scheduling; Fair-share scheduling; Cloud computing; Two-level scheduling; Rate-monotonic scheduling","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":[0.001366349,0.00103159,0.001126722,0.0004299408,0.000577188,0.001272188,0.002300376,0.001129389,0.003594276],"category_scores_gemma":[0.004293595,0.0006048578,0.0004856543,0.0003853714,0.001032803,0.00120276,0.00164507,0.001865315,0.0007182573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001467798,"about_ca_system_score_gemma":0.003026157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008750648,"about_ca_topic_score_gemma":0.01441831,"domain_scores_codex":[0.9994069,0.0001736514,0.00002240769,0.0001481533,0.00009279372,0.000156065],"domain_scores_gemma":[0.998603,0.0007277344,0.0001080654,0.0001770534,0.0001826049,0.0002015302],"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":[0.0002110252,0.0001250593,0.0007439253,0.00006443448,0.00003379299,0.00005782978,0.00005945477,0.9398025,0.001792491,0.006458889,0.004266515,0.04638418],"study_design_scores_gemma":[0.00001348128,0.00001041433,0.00002079503,0.000001988971,0.000001827276,0.00000236121,0.000004032327,0.9983311,0.0001546086,0.001289759,0.0001676982,0.000001960239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0665926,0.000727605,0.9185343,0.0009008076,0.0002851526,0.0001431429,0.0001697002,0.003960326,0.008686378],"genre_scores_gemma":[0.8247669,0.0001538707,0.168939,0.0006342205,0.00009526345,0.0001651536,0.0002418487,0.000402283,0.004601511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008750648,"threshold_uncertainty_score":0.01739943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01392785714281857,"score_gpt":0.2505494099415144,"score_spread":0.2366215527986958,"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."}}