Google’s data centers power factor estimated by neural network

Google says it has created a bit of software that lets it predict with 99.6 per cent accuracy how efficiently its data centers consume electricity, allowing it to make subtle tweaks to reduce consumption.

The company gave details on its neural network on Wednesday in a blog post. The project began as one of Google’s vaunted “20 per cent projects” by engineer Jim Gao, who decided to apply machine learning to the problem of predicting how the power usage effectiveness of Google’s data centers would change in response to tweaking one of 19 different inputs.

Power usage effectiveness (PUE) reflects the proportion of power that goes to the stuff supporting its computers versus the power which makes it into the servers and storage and networking boxes in the racks.

google-datacenter

For Google, lowering its PUE is a crucial way for the company to decrease its voluminous electricity bills. Gao’s machine learning approach helped it do this and was effective enough for Google to use in production.

“For example, a couple months ago we had to take some servers offline for a few days – which would normally make that data center less energy efficient,” the company explained in a blog post. “But we were able to use Jim’s models to change our cooling setup temporarily – reducing the impact of the change on our PUE for that time period. Small tweaks like this, on an ongoing basis, add up to significant savings in both energy and money.”

The neural network used for these data center predictions used 19 inputs, each coming with around 180,000 data points that had been gathered over the course of two years. The input data included things like the total server IT load in kilowatts, the total number of condenser water pumps running, the mean heat exchange approach temperature, the outdoor wind speed in miles per hour, and so on.

Read the article on The Register and the blog post.

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