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UID:submissions.supercomputing.org_SC20_sess166_pap363@linklings.com
SUMMARY:SEFEE: Lightweight Storage Error Forecasting in Large-Scale Enterp
 rise Storage Systems
DESCRIPTION:Paper\n\nSEFEE: Lightweight Storage Error Forecasting in Large
 -Scale Enterprise Storage Systems\n\nYazdi, Lin, Yang, Yan\n\nWith the rap
 id growth in scale and complexity, today's enterprise storage systems need
  to deal with significant amounts of errors. Existing proactive methods ma
 inly focus on machine learning techniques trained on SMART measurements. S
 uch methods, however, are usually expensive to use in practice and can onl
 y be applied to limited types of errors with a limited scale. We collected
  more than 23 million storage events from 87 deployed NetApp-ONTAP systems
  managing 14,371 disks for two years, and propose a lightweight training-f
 ree storage error forecasting method; SEFEE. SEFEE employs tensor decompos
 ition to directly analyze storage error-event logs and perform online erro
 r prediction for all error types in all storage nodes. SEFEE explores hidd
 en spatiotemporal information that is deeply embedded in the global scale 
 of storage systems to achieve record breaking error forecasting accuracy w
 ith minimal prediction overhead.\n\nTag: Fault Tolerance, Reliability and 
 Resiliency, Storage\n\nRegistration Category: Tech Program Reg Pass
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