Abstract
Monitoring supercomputing facilities tends to be very time consuming and error-prone as the size of the collected data and the number of supervised devices do not cease to increase. In this paper, we propose a methodology to supervise those facilities based on measurements performed on devices at different levels of the infrastructure. Through its three phases -raw data cleaning, ML-based processing and visualisation using our developed tool- it facilitates the supervision of the computing center facilities and helps detecting irregular behaviours leading to manual correction actions. The case of the energy consumption is considered to illustrate the usefulness of this methodology and highlight its valuable results but it can be applied to any other target metric.
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Notes
- 1.
CEA: “Commissariat à l’énergie atomique et aux énergies alternatives” for French Alternative Energies and Atomic Energy Commission.
- 2.
Partnership for Advanced Computing in Europe.
- 3.
Density-Based Spatial Clustering Applications with Noise.
- 4.
Hierarchical Density-Based Spatial Clustering Applications with Noise.
- 5.
Hierarchical Agglomerative Clustering.
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Anton, L., Willemot, S., Gougeaud, S., Zertal, S. (2023). ML-Based Methodology for HPC Facilities Supervision. In: Bienz, A., Weiland, M., Baboulin, M., Kruse, C. (eds) High Performance Computing. ISC High Performance 2023. Lecture Notes in Computer Science, vol 13999. Springer, Cham. https://doi.org/10.1007/978-3-031-40843-4_23
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