Abstract
In order to better manage the cooling infrastructure in a data center with multiple computer room air conditioning (CRAC) units, the relationship between CRAC settings and temperature at various locations in the data center needs to be accurately and reliably determined. Usually this is done via a commissioning process which is both time consuming and disruptive. In this paper, we describe a machine learning based technique to model rack inlet temperature sensors in a data center as a function of CRAC settings. These models can then be used to automatically estimate thermal correlation indices (TCI) at any particular CRAC settings. We have implemented a prototype of our methodology in a real data center with eight CRACs and several hundred sensors. The temperature sensor models developed have high accuracy (mean RMSE error is 0.2°C). The results are validated using manual commissioning, demonstrating the effectiveness of our techniques in estimating TCI and in determining thermal zones or regions of influence of the CRACs.
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