{"id":"W2808454856","doi":"10.1109/icdcs.2018.00049","title":"Speeding Up Multi-CDN Content Delivery via Traffic Demand Reshaping","year":2018,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Simon Fraser University","funders":"","keywords":"Content delivery; Multihoming; Content delivery network; Computer science; Computer network; Latency (audio); Peering; Content distribution; Server; Delivery Performance; The Internet; Telecommunications; World Wide Web; Business","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":[0.001024879,0.0007502908,0.0006262825,0.0008777141,0.0008980879,0.0008962214,0.001165904,0.0006748115,0.001690081],"category_scores_gemma":[0.002161578,0.0002973843,0.0003645531,0.000860917,0.0004189562,0.00145444,0.001398363,0.0007859872,0.0005732627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008368691,"about_ca_system_score_gemma":0.0009638066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004759078,"about_ca_topic_score_gemma":0.005147846,"domain_scores_codex":[0.9994374,0.0001310748,0.0000242217,0.000113093,0.0001394301,0.0001548871],"domain_scores_gemma":[0.9988783,0.0003576739,0.0001543167,0.0002099608,0.0002999352,0.00009975758],"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":[0.0006118382,0.0004721899,0.003326604,0.000193089,0.0000725721,0.000450261,0.00037773,0.5222738,0.09112602,0.008758146,0.01008567,0.3622521],"study_design_scores_gemma":[0.00001533603,0.00007121258,0.0004951517,0.000006049998,0.00001120954,0.0001116939,0.00008717055,0.9871278,0.007291139,0.0021843,0.002585644,0.00001319313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.186272,0.0006007466,0.8011683,0.0005796441,0.0002204172,0.0002017689,0.0001953002,0.003044581,0.007717356],"genre_scores_gemma":[0.869142,0.0001760955,0.128346,0.0001417932,0.00005873744,0.00005866584,0.0001978227,0.0001147179,0.001764124],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004759078,"threshold_uncertainty_score":0.009462774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09742156189527564,"score_gpt":0.2633326256064271,"score_spread":0.1659110637111514,"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."}}