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DTSTART;TZID=America/Denver:20231115T100000
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UID:submissions.supercomputing.org_SC23_sess303_rpost227@linklings.com
SUMMARY:Investigating Anomalies in Compute Clusters: An Unsupervised Learn
 ing Approach
DESCRIPTION:Yiyang Lu and Jie Ren (College of William & Mary); Yasir Alana
 zi, Ahmed Mohammed, Diana McSpadden, Laura Hild, Mark Jones, Wesley Moore,
  Malachi Schram, and Bryan Hess (Thomas Jefferson National Accelerator Fac
 ility); and Evgenia Smirni (College of William & Mary)\n\nAs compute clust
 ers used for running batch jobs continue to grow in scale and complexity, 
 the frequency of anomalies significantly increases. Timely detection of an
 omalous events has become vital to maintain system efficiency and availabi
 lity. Our study presents an attention-based graph neural network (GNN) to 
 detect anomalies in clusters at the compute node level and provide detaile
 d root cause analysis to pinpoint issues. Evaluating on real-world dataset
 s, attention-based GNN shows its ability to accurately detect and localize
  anomalies.\n\nTag: Artificial Intelligence/Machine Learning, Architecture
  and Networks, Heterogeneous Computing, I/O and File Systems, Performance 
 Measurement, Modeling, and Tools, Post-Moore Computing, Programming Framew
 orks and System Software, Quantum Computing\n\nRegistration Category: Tech
  Program Reg Pass, Exhibits Reg Pass\n\n
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