{"id":"W4315629602","doi":"10.1109/globecom48099.2022.10001580","title":"QoS-based Task Replication for Alleviating Uncertainty in Edge Computing","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Karush–Kuhn–Tucker conditions; Enhanced Data Rates for GSM Evolution; Reliability (semiconductor); Replication (statistics); Context (archaeology); Task (project management); Edge device; Distributed computing; Edge computing; Maximization; Computational complexity theory; Quality of service; Replica; Mathematical optimization; Computer network; Cloud computing; Algorithm; Artificial intelligence; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001427791,0.0007312378,0.0008315532,0.0004127668,0.0008163782,0.001115418,0.001509213,0.0006417258,0.001823817],"category_scores_gemma":[0.003813086,0.0002525729,0.0004888922,0.0006543114,0.0006022112,0.001711945,0.001536852,0.001047997,0.0003660769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006867789,"about_ca_system_score_gemma":0.00103215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001709122,"about_ca_topic_score_gemma":0.001978965,"domain_scores_codex":[0.9990933,0.0003127806,0.00005123642,0.0001695746,0.0002036859,0.0001694842],"domain_scores_gemma":[0.9981992,0.0008640728,0.0001804702,0.0003038866,0.0003153875,0.0001370134],"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":[0.0006559541,0.0001746325,0.001530155,0.0003165192,0.00005710145,0.0004363747,0.0003605139,0.8103403,0.03540962,0.0284891,0.00512024,0.1171095],"study_design_scores_gemma":[0.00001507532,0.00008044753,0.0001676223,0.000008868884,0.00001055441,0.00008856574,0.00004796316,0.9873424,0.002909113,0.007964698,0.001352776,0.00001192184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09426942,0.001606892,0.8972642,0.0005216954,0.0001953649,0.0001206562,0.00011481,0.0007383724,0.0051685],"genre_scores_gemma":[0.9299766,0.0004062846,0.06750156,0.0001126968,0.00007876036,0.00007864455,0.0000947533,0.00007963778,0.001671022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001823817,"threshold_uncertainty_score":0.007550955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05825631538762528,"score_gpt":0.3244304618272186,"score_spread":0.2661741464395933,"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."}}