{"id":"W4312389599","doi":"10.1109/tmc.2022.3223119","title":"QoE-Aware Decentralized Task Offloading and Resource Allocation for End-Edge-Cloud Systems: A Game-Theoretical Approach","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":137,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Computer science; Cloud computing; Nash equilibrium; Server; Distributed computing; Computation offloading; Quality of experience; Mobile edge computing; Potential game; Mobile device; Game theory; Task (project management); Resource allocation; Enhanced Data Rates for GSM Evolution; Edge computing; Computer network; Mathematical optimization; Quality of service; Artificial intelligence","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":[0.001330022,0.001221766,0.001532438,0.0005795629,0.0009230949,0.001657036,0.001772007,0.001224832,0.002080878],"category_scores_gemma":[0.002863401,0.0005264108,0.000655856,0.0006862577,0.001418629,0.001695055,0.001448471,0.001403328,0.0001833522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002089401,"about_ca_system_score_gemma":0.002556007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007416158,"about_ca_topic_score_gemma":0.00663654,"domain_scores_codex":[0.9989177,0.000417851,0.00003205142,0.000171422,0.0002244921,0.0002365429],"domain_scores_gemma":[0.99872,0.0008293564,0.0001052368,0.00005911133,0.0001791724,0.0001070592],"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":[0.00007226304,0.00009288019,0.0002799733,0.00007583741,0.0000291966,0.0001321646,0.00007546189,0.9415132,0.002141244,0.04545144,0.001072739,0.009063591],"study_design_scores_gemma":[0.00000606081,0.0000140821,0.00004173982,0.000002719119,0.000003542634,0.00001630395,0.00001759593,0.9934167,0.0001121048,0.006161373,0.0002038043,0.000004030188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01994125,0.0002836042,0.9728824,0.000376989,0.00006120959,0.0001733788,0.00005195588,0.00006708705,0.006162099],"genre_scores_gemma":[0.9168441,0.0005082904,0.07849814,0.0001899116,0.00008351111,0.0002552006,0.00004972932,0.00004442235,0.003526707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007416158,"threshold_uncertainty_score":0.01515973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01351600933497423,"score_gpt":0.2399856508324441,"score_spread":0.2264696414974699,"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."}}