{"id":"W4206275890","doi":"10.1109/tnse.2021.3132556","title":"Ensuring Profit and QoS When Dynamically Embedding Delay-Constrained ICN and IP Slices for Content Delivery","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University; Agence Nationale de la Recherche","keywords":"Computer science; Quality of service; Computer network; Profit maximization; The Internet; Embedding; Integer programming; Profit (economics); Content delivery; Distributed computing; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008271797,0.0008979051,0.0005178257,0.0003907262,0.0007152474,0.001178736,0.0007342656,0.0006962061,0.001125632],"category_scores_gemma":[0.002135836,0.0002657862,0.0002277285,0.0004773877,0.0005318928,0.001403019,0.000802846,0.0005722203,0.0001491741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001644345,"about_ca_system_score_gemma":0.00201327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003987845,"about_ca_topic_score_gemma":0.005864624,"domain_scores_codex":[0.9994759,0.0001560101,0.00001834726,0.00006025032,0.0001004477,0.0001890418],"domain_scores_gemma":[0.9991561,0.000428575,0.0001218951,0.00007622781,0.0001130969,0.0001041135],"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":[0.0001643184,0.0001219586,0.001420545,0.0001111539,0.00002383864,0.0003346922,0.0001082397,0.8938047,0.01818812,0.0193534,0.001548749,0.06482034],"study_design_scores_gemma":[0.00001390095,0.0001163752,0.0003523135,0.00001404339,0.00002125701,0.0001544986,0.0001719207,0.9807338,0.00732166,0.009430993,0.001657876,0.00001148502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.246432,0.0007308833,0.7393293,0.0006040513,0.00007464236,0.000243443,0.0001246365,0.0004681574,0.01199296],"genre_scores_gemma":[0.9073463,0.0002997195,0.0910711,0.00004888579,0.00001581562,0.00004003023,0.00005044289,0.00005311758,0.001074619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003987845,"threshold_uncertainty_score":0.01193058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01852826870807884,"score_gpt":0.2103121408699017,"score_spread":0.1917838721618229,"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."}}