{"id":"W3013536937","doi":"10.1109/tnet.2020.2979807","title":"Efficient Computing Resource Sharing for Mobile Edge-Cloud Computing Networks","year":2020,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Networking","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":251,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Higher Education Discipline Innovation Project; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Compute Canada","keywords":"Cloud computing; Utility computing; Computer science; Mobile edge computing; Distributed computing; Profit maximization; Edge computing; Mobile cloud computing; Cloud testing; Cloud computing security; Computer network; Profit (economics); Microeconomics; Economics; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0009455246,0.0005253579,0.0005710587,0.0001881669,0.001869037,0.0006024839,0.002207877,0.0002217178,0.00000332759],"category_scores_gemma":[0.00003016912,0.0005826147,0.0004189672,0.001433876,0.00007181369,0.0001322852,0.0001628072,0.0009424278,0.00002944947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001678003,"about_ca_system_score_gemma":0.00006246965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001118167,"about_ca_topic_score_gemma":0.000001332429,"domain_scores_codex":[0.9957727,0.0001316678,0.0008578243,0.00141884,0.0004545826,0.001364428],"domain_scores_gemma":[0.9969382,0.001195543,0.0003314626,0.001004814,0.0001322754,0.0003977419],"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":[0.00002809573,0.00006572354,0.00005701426,0.00004028205,0.00004757609,0.000009540433,0.001845647,0.7904276,0.00004553238,0.00005838712,0.00135871,0.2060159],"study_design_scores_gemma":[0.0007556719,0.0002310299,0.00003061956,0.000321084,0.00003763501,0.00002124586,0.00008540262,0.972948,0.000272409,0.00009546476,0.02459884,0.0006026399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0191274,0.000374373,0.9595454,0.0006012223,0.01790654,0.0008235594,8.865269e-7,0.001185636,0.0004349805],"genre_scores_gemma":[0.9311091,0.000007612638,0.05147619,0.001750339,0.01548717,0.00002852939,0.000006228402,0.00009293656,0.00004187175],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9119817,"threshold_uncertainty_score":0.9996625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03509039645127045,"score_gpt":0.2591921747431791,"score_spread":0.2241017782919087,"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."}}