{"id":"W3082511424","doi":"10.1109/sbac-pad49847.2020.00019","title":"An Optimal Model for Optimizing the Placement and Parallelism of Data Stream Processing Applications on Cloud-Edge Computing","year":2020,"lang":"en","type":"preprint","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cloud computing; Computer science; Stream processing; Distributed computing; Edge computing; Software deployment; Data stream mining; Data parallelism; Edge device; Latency (audio); Enhanced Data Rates for GSM Evolution; Data stream; Parallelism (grammar); Integer programming; Data processing; Parallel computing; Algorithm; Database; Operating system; Data mining","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":[0.00137746,0.001491674,0.001438141,0.000545417,0.0004887051,0.002179269,0.001617865,0.001889705,0.004014139],"category_scores_gemma":[0.003257471,0.00101529,0.0008476259,0.0009942709,0.001019138,0.001321476,0.001016757,0.001870363,0.0004478153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002374797,"about_ca_system_score_gemma":0.002818497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01387553,"about_ca_topic_score_gemma":0.01047774,"domain_scores_codex":[0.9993275,0.0002139983,0.00002341625,0.0001329557,0.000138906,0.0001631336],"domain_scores_gemma":[0.9986367,0.0008739443,0.000136503,0.0000416632,0.0001990825,0.0001121237],"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":[0.00002436485,0.00001461313,0.00007597047,0.00001796949,0.000004836699,0.00001646803,0.000005883341,0.9957393,0.0002248549,0.002503712,0.0003085534,0.001063473],"study_design_scores_gemma":[0.000004659389,0.00000532088,0.00001899386,0.00000154669,0.000001673644,0.000001499844,0.000002995391,0.9990444,0.00005005213,0.0007886853,0.0000791266,0.000001075341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05089162,0.0006441031,0.9352902,0.001244418,0.0001268236,0.0001972068,0.0005049306,0.0003180388,0.0107827],"genre_scores_gemma":[0.8420394,0.0007606929,0.1448835,0.0003138816,0.00008872442,0.0005045062,0.0004164354,0.0001787848,0.01081403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01387553,"threshold_uncertainty_score":0.0275895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1275006074502994,"score_gpt":0.3528406061679671,"score_spread":0.2253399987176677,"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."}}