Speaker
Description
The LHCb data centre is a key element of the experiment’s Data Acquisition (DAQ) system, while also supporting other computing tasks when not dedicated to DAQ. This project investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to further improve its efficiency and sustainability. It focuses on two main aspects: Cooling Optimization, evaluating AI techniques for controlling the cooling infrastructure and achieving reductions in PUE while maintaining performance and AI-Driven Resource Allocation, dynamically adjusting computing resources according to workload demand. In this way, computing nodes can be reassigned to other tasks when possible, maximizing resource utilization, and powered down only when no useful work is available, thereby reducing unnecessary energy consumption. Both aspects aim at reducing the environmental impact of the LHCb computing infrastructure while making the best possible use of all available computing resources.
CERN group/ Experiment
LHCb Online
| Working area | Area 7: Experimental Technologies |
|---|---|
| Project goals | Evaluate the feasibility and effectiveness of AI/ML techniques for optimizing cooling systems and resource allocation in the LHCb data centre in order to reduce energy consumption and improve sustainability while maintaining computing performance. |
| Timeline | 2 years from 2026 |
| Available person power | 0.5 FTE (QUEST) |
| Additional person power request | 0 |
| Is this an already ongoing activity? | No |
| Indicative hardware resources needs | Development environment (Jupyter Notebook) with GPUs and access to the LHCb online network and the possibility to deploy models or production pipelines. A self-contained solution as Kubeflow would be ideal. |