Anomaly detection and virtual reality visualisation in supercomputers
Por favor, use este identificador para citar o enlazar este ítem:
http://hdl.handle.net/10045/134549
Título: | Anomaly detection and virtual reality visualisation in supercomputers |
---|---|
Autor/es: | Mulero Pérez, David | Benavent-Lledó, Manuel | Azorin-Lopez, Jorge | Marcos-Jorquera, Diego | Garcia-Rodriguez, Jose |
Grupo/s de investigación o GITE: | Arquitecturas Inteligentes Aplicadas (AIA) | Undefined |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | Anomaly detection | Virtual reality and Machine learning | Supercomputing |
Fecha de publicación: | 22-may-2023 |
Editor: | Springer Nature |
Cita bibliográfica: | The International Journal of Advanced Manufacturing Technology. 2023. https://doi.org/10.1007/s00170-023-11255-x |
Resumen: | Anomaly detection is the identification of events or observations that deviate from the expected behaviour of a given set of data. Its main application is the prediction of possible technical failures. In particular, anomaly detection on supercomputers is a difficult problem to solve due to the large scale of the systems and the large number of components. Most research works in this field employ machine learning methods and regression models in a supervised fashion, which implies the need for a large amount of labelled data to train such systems. This work proposes the use of autoencoder models, allowing the problem to be approached with semi-supervised learning techniques. Two different model training approaches are compared. The former is a model trained with data from all the nodes of a supercomputer. In the latter approach, observing significant differences between nodes, one model is trained for each node. The results are analysed by evaluating the positive and negative aspects of each approach. On the other hand, a replica of the Marconi 100 supercomputer is developed in a virtual reality environment that allows the data from each node to be visualised at the same time. |
Patrocinador/es: | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. We would like to thank “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the MoDeaAS project (grant PID2019-104818RB-I00). Furthermore, we would like to thank the University of Skövde and to ASSAR Innovation Arena for their support to develop this work. |
URI: | http://hdl.handle.net/10045/134549 |
ISSN: | 0268-3768 (Print) | 1433-3015 (Online) |
DOI: | 10.1007/s00170-023-11255-x |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1007/s00170-023-11255-x |
Aparece en las colecciones: | INV - AIA - Artículos de Revistas INV - Undefined - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
Mulero-Perez_etal_2023_IntJAdvManufTechnol.pdf | 3,36 MB | Adobe PDF | Abrir Vista previa | |
Este ítem está licenciado bajo Licencia Creative Commons