Open Access
| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 07007 | |
| Number of page(s) | 7 | |
| Section | Facilities and Virtualization | |
| DOI | https://doi.org/10.1051/epjconf/202429507007 | |
| Published online | 06 May 2024 | |
- Lyndon Evans and Philip Bryant, LHC Machine, JINST 3 S08001(2008). [CrossRef] [Google Scholar]
- The CMS Collaboration, et. al., The CMS experiment at the CERN LHC, JINST 3 S08004(2008). [Google Scholar]
- A. Dorigo, P. Elmer, F. Furano, and A. Hanushevsky, Xrootd - A highly scalable architecture for data access, WSEAS Transactions on Computers (2005). [Google Scholar]
- M. Barisits, et.al., Rucio: Scientifc Data Management, Computing and Software for Big Science (2019) 3:11. [Google Scholar]
- D. Weitzel, et. al., XRootD Monitoring Collector, https://zenodo.org/record/4670589 [Google Scholar]
- B. Garrido, A. Forti, D. Weitzel, J. Andreeva, and S. McKee, New XRootD Monitoring implementation, To Be Submitted to the CHEP2023 Proceedings. [Google Scholar]
- D. Weitzel, et. al., XRootD Monitoring Shoveler, https://zenodo.org/record/8269867 [Google Scholar]
- R. Pordes, D. Petravick, B. Kramer, D. Olson, M. Livny, A. Roy, P. Avery, K. Blackburn, T. Wenaus, F. Würthwein, I. Foster, R. Gardner, M. Wilde, A. Blatecky, J. McGee, and R. Quick, The open science grid, J. Phys. Conf. Ser., 78, 012057(2007). https://doi.org/10.1088/1742-6596/78/1/012057 [NASA ADS] [CrossRef] [Google Scholar]
- F. Pedregosa, et. al., Scikit-learn: Machine Learning in Python, JMLR 12, pp. 2825-2830(2011). [Google Scholar]
- F. Chollet, et. al., Keras, Available at https://github.com/fchollet/keras(2015) and Timeseries Anomaly Detection Example, Available at https://keras.io/examples/timeseries/timeseries_anomaly_detection/ [Google Scholar]
- E. Torres, Applying Deep Learning Algorithms to Alarm/Anomaly Detection for Grid Jobs, FNAL SIST Program (2017) [Google Scholar]
- V. Sharma, Keras predicting random file transfer durations, https://github.com/vyomshm/Keras-predicting-random-file-transfer-durations (2017) [Google Scholar]
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