About
To mitigate the rising energy demands and CO₂ emissions of large-scale scientific computing, this study evaluated energy-aware resource management at the WLCG PIC Tier-1 site. We initially used machine learning to optimize node drainage by routing short jobs during peak energy prices. However, this complex scheduling approach proved impractical, requiring constant retraining for fluctuating workloads.
Consequently, we identified real-time CPU frequency scaling as a superior alternative. Unlike complex scheduling, adjusting CPU frequency is straightforward, delivers immediate power reductions, and ensures controllable performance degradation. Node-level experiments validated this approach, quantifying the exact relationship between frequency, compute performance, and power draw. By estimating facility-wide savings based on these results, this research delivers a practical, scalable framework for automated, center-wide energy optimization at PIC and other WLCG sites.
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The talk
Credits and licence
- Institution
- CERN
- Speaker
- Jose Flix Molina
- Year
- 2026
- Language
- English
- Subjects
- Mathematics, Business and management
- 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 Tweens profiles and older, judged from what the source says about it. How courses are placed