{"id":"W4310815533","doi":"10.48550/arxiv.2107.04748","title":"Resilient Edge Service Placement under Demand and Node Failure Uncertainties","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Age of Information Optimization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Workload; Mathematical optimization; Distributed computing; Node (physics); Resource allocation; Enhanced Data Rates for GSM Evolution; Service (business); Set (abstract data type); Resource (disambiguation); Edge computing; Operations research; Computer network; Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001257137,0.0008335905,0.001002571,0.0003688621,0.0005142147,0.00115526,0.0009515297,0.001539477,0.001438665],"category_scores_gemma":[0.003657229,0.0005565157,0.0005817603,0.0005723526,0.0008410656,0.001326467,0.00124491,0.001299838,0.000180615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001297651,"about_ca_system_score_gemma":0.0009554274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007170636,"about_ca_topic_score_gemma":0.004079049,"domain_scores_codex":[0.9993107,0.0002216086,0.00002432162,0.0001512119,0.0001221137,0.0001700772],"domain_scores_gemma":[0.9986262,0.000889795,0.0001939896,0.00008730921,0.0001214671,0.00008125868],"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":[0.00001542693,0.000006468324,0.0001304317,0.000007207917,0.000004004903,0.00003061255,0.000007987405,0.9957402,0.0003581984,0.002131589,0.00009047616,0.001477366],"study_design_scores_gemma":[0.000001599299,0.00000689235,0.000043196,9.366693e-7,0.000001226694,0.000004220561,0.000007483323,0.9982393,0.0001397562,0.001507461,0.00004633291,0.000001626116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09804064,0.0001852514,0.8977297,0.0004248313,0.00003422195,0.00005148014,0.0001116816,0.0001497664,0.003272387],"genre_scores_gemma":[0.97347,0.0001332239,0.02478394,0.00004055836,0.00001793196,0.0000426417,0.00006661349,0.00003125588,0.001413743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007170636,"threshold_uncertainty_score":0.01425785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03616013504032411,"score_gpt":0.1754315684299849,"score_spread":0.1392714333896608,"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."}}