Analysis of XDMoD/SUPReMM Data Using Machine Learning Techniques
Steven M. Gallo, Joseph Patrick White, Robert L. DeLeon, Thomas R. Furlani, Helen Ngo, Abani Patra, Matthew D. Jones, Jeffrey T. Palmer, Nikolay A. Simakov, Jeanette M. Sperhac, Martins Innus, Thomas Yearke, Ryan Rathsam · 2015
Machine learning techniques were applied to job accounting and performance data for application classification. Job data were accumulated using the XDMoD monitoring technology named SUPReMM, they consist of job accounting information, application information from Lariat/XALT, and job performance data from TACC_Stats. The results clearly demonstrate that community applications have characteristic signatures which can be exploited for job classification. We conclude that machine learning can assist in classifying jobs of unknown application, in characterizing the job mixture, and in harnessing the variation in node and time dependence for further analysis.