{"id":"W4392189677","doi":"10.36227/techrxiv.170905717.70509967/v1","title":"CoRaiS: Lightweight Real-Time Scheduler for Multi-Edge Cooperative Computing","year":2024,"lang":"en","type":"preprint","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Distributed computing; Scheduling (production processes); Edge computing; Schedule; Integer programming; Enhanced Data Rates for GSM Evolution; Response time; Quality of service; Real-time computing; Computer network; Artificial intelligence; Mathematical optimization; Algorithm; 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":[0.001095604,0.0007293675,0.0004841222,0.0003424725,0.0005301317,0.0009720789,0.001632808,0.0005184317,0.00252158],"category_scores_gemma":[0.0026303,0.0002448049,0.0003517857,0.0004001191,0.0005115964,0.001100167,0.001137821,0.001386201,0.0006845836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001104609,"about_ca_system_score_gemma":0.002427793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005887785,"about_ca_topic_score_gemma":0.007224555,"domain_scores_codex":[0.9994521,0.0001088079,0.00003628609,0.0001140948,0.0001900095,0.00009865144],"domain_scores_gemma":[0.9991356,0.0002510475,0.00008781028,0.0002035447,0.0001942122,0.0001278382],"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":[0.0007064754,0.0003536587,0.00280718,0.0001923898,0.00008227933,0.0002554806,0.0002646639,0.6891869,0.02633599,0.05638933,0.0199919,0.2034338],"study_design_scores_gemma":[0.00001627204,0.0000241999,0.00009525877,0.000002563553,0.000004758026,0.00001361552,0.000008204363,0.9903713,0.00230069,0.004805466,0.002351263,0.000006515817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01623054,0.0001825407,0.9742926,0.0001815343,0.0001267031,0.0001422962,0.0001071445,0.00608036,0.002656288],"genre_scores_gemma":[0.7379549,0.000251813,0.2553856,0.0002329567,0.0001184351,0.0002666423,0.0004067625,0.0004604056,0.004922561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005887785,"threshold_uncertainty_score":0.01170701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04955335231626892,"score_gpt":0.3169927182707755,"score_spread":0.2674393659545066,"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."}}