{"id":"W4312128045","doi":"10.52953/ehjp3291","title":"Intelligent proactive fault tolerance at the edge through resource usage prediction","year":2022,"lang":"en","type":"article","venue":"ITU Journal on Future and Evolving Technologies","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"European Commission","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Distributed computing; Fault tolerance; Benchmark (surveying); Edge computing; Resource (disambiguation); Node (physics); Resource allocation; Quality of service; CloudSim; Reliability (semiconductor); Mean squared error; Machine learning; Artificial intelligence; Computer network; Power (physics); Cloud computing; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005807939,0.0001841502,0.0001703673,0.0001287064,0.002491216,0.0002944425,0.001148481,0.00009357841,0.00000639131],"category_scores_gemma":[0.00008730105,0.0001207433,0.00008327951,0.0004720855,0.0001155769,0.0003851905,0.001436142,0.001243799,0.000006791048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002475514,"about_ca_system_score_gemma":0.00003760705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003114162,"about_ca_topic_score_gemma":8.833396e-7,"domain_scores_codex":[0.9985052,0.0001123849,0.0002563937,0.0003505495,0.0004180216,0.0003575006],"domain_scores_gemma":[0.9991081,0.0001339514,0.0002094213,0.0004575518,0.00006096657,0.00003005779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001093249,0.0001566838,0.001374056,0.00002271819,0.0001308539,0.0001687398,0.01342649,0.003079101,0.000849807,0.005648537,0.3995364,0.5754973],"study_design_scores_gemma":[0.0002806644,0.0006067636,0.001310532,0.00006777579,0.00001644504,0.001142094,0.004964626,0.01321652,0.004906717,0.01081203,0.9623949,0.0002809543],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6290154,0.04456384,0.1459323,0.1188481,0.04228639,0.001292708,0.00000794825,0.004389742,0.01366351],"genre_scores_gemma":[0.9824436,0.001286863,0.009676654,0.001447882,0.003930919,0.00006167668,0.000003104245,0.0000321921,0.001117143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5752163,"threshold_uncertainty_score":0.9988074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01639744865778712,"score_gpt":0.2339954563293717,"score_spread":0.2175980076715846,"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."}}