{"id":"W3013672921","doi":"10.1109/wcnc45663.2020.9120597","title":"Joint Computation Offloading, SFC Placement, and Resource Allocation for Multi-Site MEC Systems","year":2020,"lang":"en","type":"preprint","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Computation offloading; Computer science; Mobile edge computing; Distributed computing; Server; Resource allocation; Energy consumption; Computation; Cloud computing; Mathematical optimization; Edge computing; Computer network; Algorithm; Engineering","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":[0.000506634,0.0009675781,0.0008974685,0.0004237187,0.0006424166,0.0009979022,0.0007119431,0.0008308641,0.002621507],"category_scores_gemma":[0.001249528,0.0003534278,0.0004905409,0.0006770126,0.0004839803,0.0007102485,0.0008686152,0.0006550942,0.0002961225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001108839,"about_ca_system_score_gemma":0.001245455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007979523,"about_ca_topic_score_gemma":0.01138597,"domain_scores_codex":[0.9995918,0.0001140712,0.00001394457,0.00007063037,0.00008473478,0.0001248763],"domain_scores_gemma":[0.9996351,0.0001844419,0.00003395298,0.00004676846,0.00005410135,0.00004559985],"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":[0.00007157921,0.00003994503,0.0003484348,0.00003371931,0.00001344869,0.00007329127,0.00002131413,0.9717652,0.0029958,0.002362431,0.0009258486,0.02134896],"study_design_scores_gemma":[0.000003896455,0.00001159428,0.00008524828,0.000001824632,0.000001989917,0.00001246228,0.0000091914,0.9984345,0.000492343,0.0007728053,0.0001720241,0.000002054927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1094532,0.0004969765,0.8803561,0.000339109,0.00007563103,0.0001088193,0.0001059698,0.0005678893,0.008496277],"genre_scores_gemma":[0.8771405,0.0001717217,0.11993,0.00006526925,0.00002554474,0.00007936706,0.000123766,0.00008068313,0.002383178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007979523,"threshold_uncertainty_score":0.01586616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1050329146875447,"score_gpt":0.3045503172861821,"score_spread":0.1995174025986374,"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."}}