{"id":"W4385357917","doi":"10.3390/fi15080251","title":"Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN","year":2023,"lang":"en","type":"article","venue":"Future Internet","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Ottawa","funders":"","keywords":"Computer science; Cache; Reinforcement learning; Benchmark (surveying); Computer network; Information-centric networking; Software-defined networking; Robustness (evolution); Context (archaeology); False sharing; Distributed computing; CPU cache; Cache algorithms; 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":[0.0007236173,0.0006739438,0.0009172453,0.0003373607,0.0002862902,0.0006391881,0.00120133,0.0008228904,0.001165406],"category_scores_gemma":[0.00273267,0.0003242151,0.0003666076,0.0003842529,0.0005652056,0.0007706716,0.0006177253,0.00105911,0.0001341085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001390456,"about_ca_system_score_gemma":0.001218149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02320025,"about_ca_topic_score_gemma":0.02231555,"domain_scores_codex":[0.9997898,0.00005248822,0.00001234459,0.00005622449,0.00003708112,0.00005205135],"domain_scores_gemma":[0.9989005,0.0006480094,0.00009673949,0.00004773558,0.0002438115,0.00006329904],"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":[0.00003316027,0.0000311904,0.0006669132,0.00002151311,0.00001925755,0.00002868263,0.00001335637,0.9842152,0.0003584357,0.001692364,0.0005286277,0.01239127],"study_design_scores_gemma":[0.0000018613,0.000004714227,0.00002526427,9.868683e-7,0.000001988912,0.000001558878,9.592836e-7,0.9994923,0.00003930754,0.0004013549,0.00002889522,8.215179e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1662038,0.001639584,0.8226978,0.0009860483,0.0002544122,0.00008003227,0.0001851112,0.001262706,0.006690692],"genre_scores_gemma":[0.9764686,0.0001840835,0.02127424,0.0002114612,0.00003069144,0.00004406252,0.0001178158,0.00003139435,0.001637729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02320025,"threshold_uncertainty_score":0.04613042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01413858790375801,"score_gpt":0.2209575774238036,"score_spread":0.2068189895200456,"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."}}