{"id":"W2171918585","doi":"10.1109/ccgrid.2012.18","title":"Executing Data-Intensive Workloads in a Cloud","year":2012,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Provisioning; Workload; Computer science; Cloud computing; Distributed computing; Scheduling (production processes); Resource management (computing); Resource (disambiguation); Data center; Operating system; Computer network","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":[],"consensus_categories":[],"category_scores_codex":[0.0007623973,0.00009968994,0.0001197443,0.00009493231,0.00006258522,0.00008246909,0.001365164,0.00003083228,0.00001127868],"category_scores_gemma":[0.0000893608,0.00008110468,0.00002504298,0.000414836,0.00001807254,0.00007259644,0.002434105,0.0001322068,0.0001446726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003069355,"about_ca_system_score_gemma":0.00001064254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001376381,"about_ca_topic_score_gemma":0.00001322099,"domain_scores_codex":[0.9987954,0.00005590256,0.0001886501,0.0003147035,0.0001834813,0.0004617831],"domain_scores_gemma":[0.9986827,0.00009452652,0.00004729644,0.001053713,0.00003413716,0.00008757743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001306638,0.0005005168,0.08304086,0.00004936144,0.0000867068,0.0001086886,0.01346256,0.005636004,0.0001206299,0.1799581,0.1038823,0.6131412],"study_design_scores_gemma":[0.0006094428,0.00003719428,0.01895382,0.000118197,0.00000914379,0.00004315739,0.001828904,0.8734444,0.0001780097,0.0007512157,0.103508,0.000518495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4436818,0.001221795,0.4690599,0.006294407,0.00278617,0.0003080795,7.292289e-7,0.0006934402,0.07595371],"genre_scores_gemma":[0.9815695,0.000001730484,0.01520754,0.001535777,0.0003241341,0.000002018898,0.000001158989,0.000005482648,0.001352628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8678085,"threshold_uncertainty_score":0.3307353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04547314531723473,"score_gpt":0.2766384998726842,"score_spread":0.2311653545554495,"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."}}