Diagnosing NFS errors: Preliminary Findings from a Syslog Analysis of Bridges

Abstract

Bridges is the current main system at the Pittsburgh Supercomputing Center. Given the complexity of the system and the volume of its use, it is a very good environment for exploring the potential of machine learning techniques in studying sub-optimal performance. This short report discusses preliminary and ongoing work of a new graduate student exploring this novel realm. Our initial focus has been on learning to predict the occurrence of NFS time out errors from preceding syslog messages.