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DTSTART;TZID=America/Denver:20231112T114100
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UID:submissions.supercomputing.org_SC23_sess422_ws_hpcsysp108@linklings.co
 m
SUMMARY:Heterogeneous Syslog Analysis: There Is Hope
DESCRIPTION:Andres Quan, Leah Howell, and Hugh Greenberg (Los Alamos Natio
 nal Laboratory (LANL))\n\nHeterogeneous test-bed clusters present a unique
  challenge in identifying system hardware failures and anomalies as a resu
 lt of the variation in the ways that errors and warnings are reported thro
 ugh the system log. We present a novel approach for the real-time classifi
 cation of syslog messages, generated from a heterogeneous test-bed cluster
 , to proactively identify potential hardware issues and security events. B
 y integrating machine learning models with high-performance computing syst
 ems, our system facilitates continuous system health monitoring. <br /><br
  />The paper introduces a taxonomy for classifying system issues into acti
 onable categories of problems, while filtering out groups of messages that
  the system administrators would consider unimportant "noise". Finally we 
 experiment with using newly available large language models as a form of m
 essage classifier, and share our results and experience with doing so. Res
 ults demonstrate promising performance, and more explainable results compa
 red to currently available techniques, but the computational costs may off
 set the benefits.\n\nTag: Artificial Intelligence/Machine Learning, Cloud 
 Computing, Distributed Computing, Data Analysis, Visualization, and Storag
 e, Data Movement and Memory, Fault Handling and Tolerance, I/O and File Sy
 stems, Large Scale Systems, Performance Optimization, Resource Management,
  Security, State of the Practice\n\nRegistration Category: Workshop Reg Pa
 ss\n\nSession Chairs: Matt Bidwell (National Renewable Energy Laboratory (
 NREL)) and John Blaas (National Center for Atmospheric Research (NCAR))\n\
 n
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