{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005030202,0.0006429085,0.0004595844,0.0003113737,0.0002370015,0.0005302381,0.0011647,0.0005079739,0.0005526483],"category_scores_gemma":[0.001676026,0.0001765287,0.0002761779,0.0002451915,0.0002979724,0.001094756,0.000572516,0.0007161614,0.000171812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004847139,"about_ca_system_score_gemma":0.0006203848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0059034,"about_ca_topic_score_gemma":0.006533026,"domain_scores_codex":[0.9997709,0.00004134766,0.00001082039,0.00006308759,0.00006192079,0.00005178488],"domain_scores_gemma":[0.9995215,0.0001672906,0.0000940799,0.00007344328,0.0001135192,0.00003005461],"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":[0.0001450248,0.00009229229,0.003387742,0.00004482833,0.00005030878,0.00009134798,0.00006618454,0.8578961,0.01200267,0.001728588,0.001366877,0.123128],"study_design_scores_gemma":[0.000002103943,0.00001657143,0.0002937507,0.000002651453,0.000005956685,0.00001133174,0.000006080233,0.9966981,0.001981954,0.0007929196,0.0001849994,0.000003661808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1565804,0.0006310521,0.837053,0.0003495227,0.00008195096,0.00003833733,0.000102137,0.002556875,0.002606628],"genre_scores_gemma":[0.9698126,0.0001095426,0.02906965,0.00009241861,0.00001482615,0.00002104688,0.00007800737,0.00004143442,0.0007605697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0059034,"threshold_uncertainty_score":0.01173806,"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."}}