Speaker
Description
The Worldwide LHC Computing Grid (WLCG) provides the distributed infrastructure necessary to support both LHC and non-LHC experiments; however, the corresponding rise in energy usage presents new challenges, in particular with the upcoming HL-LHC era, where computing requirements will continue to expand significantly.
Therefore monitoring power consumption has become increasingly important, due to rising energy costs and the growing computational demand of scientific research.
Collecting accurate power consumption data across thousands of servers in diverse data centers is not trivial, and establishing a unified monitoring solution often requires additional infrastructure and maintenance. This work proposes a lightweight approach to obtain information on power consumption from computing centers by leveraging existing infrastructure, requiring minimal effort from site administrators.
The proposed method enables large-scale and continuous data collection, allowing us to construct a comprehensive model of power usage across the entire grid. Currently, no reliable metric collector exists to quantify the overall power efficiency of the WLCG. By increasing site adoption and integrating this solution, we can finally obtain these missing insights. With sufficient statistics, the model will also enable predictive capabilities, such as estimating the expected power efficiency of new or evolving sites. This approach will create a shared knowledge base that supports data-driven decisions, capacity planning, and long-term energy-efficiency strategies, while also improving overall resource accounting. Previous studies and isolated tests demonstrate that the proposed approach is both technically feasible and highly valuable for the HEP community.