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Found 3 papers, 1 papers with code
Power Modeling for Effective Datacenter Planning and Compute Management
To the best of our knowledge, this is the largest scale power modeling study of this kind, in both the scope of diverse datacenter planning and real-time management use cases, as well as the variety of hardware configurations and workload types used for modeling and validation.
Data center cooling using model-predictive control
Despite impressive recent advances in reinforcement learning (RL), its deployment in real-world physical systems is often complicated by unexpected events, limited data, and the potential for expensive failures.
Safe Exploration for Identifying Linear Systems via Robust Optimization
Motivated by the cooling of Google's data centers, we study how one can safely identify the parameters of a system model with a desired accuracy and confidence level.
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