Disk Failure Prediction in Data Centers via Online Learning

Disk failure has become a major concern with the rapid expansion of storage systems in data centers. Based on SMART (Self-Monitoring, Analysis and Reporting Technology) attributes, many researchers derive disk failure prediction models using machine learning techniques. Despite the significant developments, the majority of works rely on offline training and thereby hinder their adaption to the continuous update of forthcoming data, suffering from the 'model aging' problem. We are therefore motivated to uncover the root cause -- the dynamic SMART distribution for 'model aging', aiming to resolve the performance degradation as to pave a comprehensive study in practice.