@inproceedings{9090205981a94224811709d03ba29008,
title = "Analysis of XDMoD/SUPReMM data using machine learning techniques",
abstract = "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.",
keywords = "Application classification, HPC monitoring, Machine learning, Open XDMoD, SUPReMM, TACC-Stats, XDMoD",
author = "Gallo, \{Steven M.\} and White, \{Joseph P.\} and \{De Leon\}, \{Robert L.\} and Furlani, \{Thomas R.\} and Helen Ngo and Patra, \{Abani K.\} and Jones, \{Matthew D.\} and Palmer, \{Jeffrey T.\} and Nikolay Simakov and Sperhac, \{Jeanette M.\} and Martins Innus and Thomas Yearke and Ryan Rathsam",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Cluster Computing, CLUSTER 2015 ; Conference date: 08-09-2015 Through 11-09-2015",
year = "2015",
month = oct,
day = "26",
doi = "10.1109/CLUSTER.2015.114",
language = "English",
series = "Proceedings - IEEE International Conference on Cluster Computing, ICCC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "642--649",
booktitle = "Proceedings - 2015 IEEE International Conference on Cluster Computing, CLUSTER 2015",
address = "United States",
}