{"id":"W2962424639","doi":"10.1109/icc.2019.8761705","title":"An Optimal Peak Hour Content Server Cache Update Scheduling Algorithm for 5G HetNets","year":2019,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Cache; Markov decision process; Heuristics; Scheduling (production processes); Server; Computer network; Distributed computing; Algorithm; Markov process; Real-time computing; Mathematical optimization; Operating system","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":[0.0004497973,0.0004440623,0.0007749093,0.0003642116,0.0004665473,0.0005712335,0.0007874086,0.0005083329,0.001017031],"category_scores_gemma":[0.0009155758,0.000291299,0.0003019918,0.0005793251,0.000372925,0.0005483159,0.0003545594,0.0004103559,0.0001248196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00126591,"about_ca_system_score_gemma":0.002402765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009922932,"about_ca_topic_score_gemma":0.01061181,"domain_scores_codex":[0.9997003,0.00007111646,0.00001272359,0.00006513851,0.0000638352,0.00008687964],"domain_scores_gemma":[0.9996148,0.0001908829,0.00006640054,0.00002227869,0.00006766602,0.0000379055],"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":[0.0001253762,0.00004625231,0.0007344866,0.00002590702,0.00001985743,0.00004094867,0.00004707638,0.9611133,0.002186416,0.005040284,0.001352823,0.02926717],"study_design_scores_gemma":[0.00001024344,0.0000152859,0.0001031992,0.000001245647,0.000003517524,0.000008612145,0.00001162861,0.9982499,0.0002971001,0.001157174,0.0001393733,0.000002688053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1438458,0.0004783461,0.8508073,0.000340746,0.00006448693,0.00008620684,0.0001563412,0.0004564537,0.003764307],"genre_scores_gemma":[0.8986911,0.0001699613,0.09960828,0.00007334299,0.0000239825,0.00006532139,0.0001478253,0.00003649856,0.001183829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009922932,"threshold_uncertainty_score":0.01973033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03110782017949939,"score_gpt":0.2515341022006478,"score_spread":0.2204262820211484,"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."}}