{"id":"W4402896748","doi":"10.1109/tcc.2024.3468913","title":"QoS-Aware, Cost-Efficient Scheduling for Data-Intensive DAGs in Multi-Tier Computing Environment","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cloud Computing","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Scheduling (production processes); Distributed computing; Quality of service; Cloud computing; Parallel computing; Computer network; Operating system; Mathematical optimization","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":[0.0007669839,0.0005625906,0.0004142468,0.0005393527,0.0006849631,0.001157588,0.001030153,0.0003901753,0.001110751],"category_scores_gemma":[0.001869657,0.0002198932,0.000294662,0.0009528914,0.0003142887,0.001096363,0.0006887889,0.0004904327,0.0002854434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001329864,"about_ca_system_score_gemma":0.002447417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0076402,"about_ca_topic_score_gemma":0.01562769,"domain_scores_codex":[0.9996125,0.0001111257,0.00002746599,0.00006061857,0.0001062786,0.00008209532],"domain_scores_gemma":[0.9992856,0.0002296202,0.00006965952,0.0001488254,0.0001665715,0.00009969153],"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":[0.0003694708,0.0002167564,0.002644697,0.000205594,0.00004031541,0.0001523169,0.0001843534,0.7939566,0.03847969,0.01739552,0.005950598,0.1404041],"study_design_scores_gemma":[0.00001652782,0.0000506529,0.0004105404,0.000003099693,0.00001154315,0.00002139967,0.00004787331,0.9861563,0.004191237,0.007410155,0.001671806,0.000008949546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1771812,0.0004836117,0.8132544,0.0006244699,0.00009122497,0.0001528894,0.0004602568,0.002431259,0.00532069],"genre_scores_gemma":[0.7503533,0.0002676073,0.2465855,0.00009698491,0.00001866893,0.00006906726,0.000625742,0.0001563226,0.001826751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0076402,"threshold_uncertainty_score":0.0151915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0868995167611226,"score_gpt":0.3239101298340855,"score_spread":0.2370106130729629,"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."}}