{"id":"W2491802577","doi":"10.1109/infocom.2016.7524628","title":"Reducing access latency in erasure coded cloud storage with local block migration","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Erasure code; Computer science; Server; Latency (audio); Cloud storage; Data striping; Cloud computing; Block (permutation group theory); Erasure; File server; Distributed data store; Computer network; Distributed computing; Operating system; Decoding methods; Algorithm; Telecommunications","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":[0.000794802,0.0004395849,0.0006410122,0.0004171254,0.000837977,0.0007075742,0.001064532,0.0004846061,0.0007484601],"category_scores_gemma":[0.003371273,0.0002085848,0.0001991809,0.0008197083,0.0008841643,0.001491176,0.001012996,0.0004520135,0.0001618962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001661604,"about_ca_system_score_gemma":0.001454469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005628266,"about_ca_topic_score_gemma":0.007501505,"domain_scores_codex":[0.999509,0.0001149351,0.00002315776,0.000070054,0.0001244,0.0001585534],"domain_scores_gemma":[0.9976642,0.001202851,0.0002850443,0.0003926962,0.0003066569,0.0001485869],"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":[0.001148729,0.0001406237,0.002776715,0.0001144923,0.00002554912,0.0002171431,0.0002543116,0.8837414,0.04109891,0.008311694,0.001438626,0.06073176],"study_design_scores_gemma":[0.00001070841,0.00007659898,0.0002864634,0.000004300165,0.000003987738,0.00005123384,0.00006057791,0.9906882,0.006990305,0.001628405,0.0001917104,0.000007565429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6987299,0.0007764282,0.2968273,0.000318766,0.00004667327,0.00006765516,0.00009136527,0.001079394,0.002062647],"genre_scores_gemma":[0.9750043,0.00009307476,0.02431622,0.00003280088,0.000006673532,0.00001916973,0.00002889504,0.00003186938,0.0004671561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005628266,"threshold_uncertainty_score":0.01205581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01827926716177763,"score_gpt":0.258097494909511,"score_spread":0.2398182277477334,"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."}}