About
**Project Description**:
The Worldwide LHC Computing Grid (WLCG) is a large-scale distributed infrastructure supporting data processing and analysis for CERN experiments. Due to its complexity and the dependencies between its services, failures or degradation in individual components may affect other parts of the system. Understanding and predicting these effects could help improve its reliability and operational efficiency.
Graph Neural Networks are well suited to modelling interconnected systems. This project investigates whether the WLCG can be represented as a temporal graph and whether GNNs can use its topology to predict operational degradation. FTS monitoring data is used as an initial proxy, providing a first step towards data-driven modelling of the WLCG.
**Supervisor**: Maria Del Carmen Misa Moreira, Sofia Vallecorsa
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The talk
Credits and licence
- Institution
- CERN
- Speaker
- Pavel Khudov Yakovlev
- Year
- 2026
- Language
- English
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed