{"id":"W4293240847","doi":"10.18280/mmep.090234","title":"Task Failure Prediction Using Machine Learning Techniques in the Google Cluster Trace Cloud Computing Environment","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cloud computing; Computer science; Task (project management); Machine learning; Virtual machine; Context (archaeology); Artificial intelligence; Utility computing; Support vector machine; Distributed computing; Resource (disambiguation); Artificial neural network; Data mining; Cloud computing security; Operating system; Computer network; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001086939,0.001036869,0.0007665372,0.001912178,0.0005541093,0.0007150454,0.0009006719,0.0006303393,0.0003722386],"category_scores_gemma":[0.002854071,0.0001816393,0.0005542387,0.001767812,0.000307724,0.0008766743,0.0006168134,0.0007481516,0.0001724842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001117587,"about_ca_system_score_gemma":0.001188246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07456331,"about_ca_topic_score_gemma":0.04911335,"domain_scores_codex":[0.9994895,0.00009884914,0.00003605839,0.0001099288,0.0001663913,0.00009905657],"domain_scores_gemma":[0.9988355,0.0003977485,0.0001422873,0.0001521248,0.0003621313,0.000110277],"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":[0.0003492269,0.0002406224,0.03098372,0.00008582552,0.00008375738,0.0002272789,0.00007106301,0.9076332,0.001500955,0.0007491595,0.004061016,0.0540142],"study_design_scores_gemma":[0.000002792195,0.0000306831,0.005377175,0.00000352981,0.000006083473,0.00001519448,0.00003660755,0.9934977,0.0004339214,0.0003629164,0.0002263527,0.00000705003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9592453,0.0006657909,0.03472745,0.0004868713,0.0001158795,0.00007897077,0.001673686,0.001288568,0.001717565],"genre_scores_gemma":[0.9891939,0.0001352899,0.008603711,0.00002514481,0.00001812972,0.00002576814,0.001568213,0.00002084428,0.0004090236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07456331,"threshold_uncertainty_score":0.1482586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01468421040079742,"score_gpt":0.190158337966386,"score_spread":0.1754741275655886,"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."}}