Data center cooling using model-predictive control
Authors: Nevena Lazic, Craig Boutilier, Tyler Lu, Eehern Wong, Binz Roy, MK Ryu, Greg Imwalle
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of our MPC approach w.r.t. the existing local PID method on a large-scale DC. |
| Researcher Affiliation | Industry | Nevena Lazic, Tyler Lu, Craig Boutilier, Moonkyung Ryu Google Research {nevena, tylerlu, cboutilier, mkryu}@google.com Eehern Wong, Binz Roy, Greg Imwalle Google Cloud {ejwong, binzroy, gregi}@google.com |
| Pseudocode | No | The paper describes the control optimization and system identification process using equations and text, but it does not include a structured pseudocode block or algorithm figure. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their methodology is publicly available. |
| Open Datasets | No | The paper states that the models were trained on '3 hours of deliberate exploration data' and 'a week of historical data generated by local PID controllers', which implies custom-collected data and no mention of public availability or access. |
| Dataset Splits | Yes | Each time step corresponds to a period of 30s, and we set T = 5 based on cross-validation. |
| Hardware Specification | No | The paper describes the data center environment and its components (e.g., 'large-scale data center', 'server floor', 'AHUs'), but it does not provide specific details about the CPU, GPU, or other hardware used for training or running the control models. |
| Software Dependencies | No | The paper mentions 'Tensor Flow [1]' but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | Yes | Each time step corresponds to a period of 30s, and we set T = 5 based on cross-validation. While we optimize over the entire trajectory, we only execute the optimized control action at the first time step. Re-optimizing at each step enables us to react to changes in disturbances and compensate for model error. We specify the above objective as a computation graph in Tensor Flow [1] and optimize controls using the Adam [19] algorithm. In particular, we implement constraints by specifying controls as uc i[τ] = max(uc min, min(uc max, uc i[τ 1] + ctanh(zc i [τ]))). |