Google announced its use of Machine Learning to improve its data center PUE in May 2014 and I posted on the release. At 7x24 Exchange Fall 2014 event, 25 years of 7x24 Exchange were celebrated and Google’s Joe Kava, VP of Data Centers presented on “Google - beyond the PUE Plateau.” The keynote is one of the more interesting and insightful presentations made as Google shared information on its experience deploying Machine Learning to its data center fleet. One of the questions from the audience was “how was the first data center chosen to use Machine Learning?” A special guest in the presentation was the data center mechanical engineer who spearheaded the project, Jim Gao. His answer. The data center that has most clean data to work with.

Jim Gao and Joe Kava, 7x24 Exchange Fall Conference.

So what can this 25 year old mechanical engineer do with Machine Learning? Below is data showing PUE, Wet Bulb, and Cooling Temperature across a range. The Blue areas are good, green not as good, yellow and red are bad.

Some of you may be saying big deal. I can figure out how to run the mechanical systems with a low PUE at a given wet bulb temperature to hit a given cooling temperature. Well the above was a graph to illustrate what can be seen looking at performance data. What is beyond our ability to see is working out the best way to run your mechanical systems with 19 Input Variables. The below are the 19 inputs to the Predictive PUE Machine Learning system to figure out the lowest energy consumption.

FYI, this predictive PUE system does not have autonomous control over mechanical systems. It does provide information to the data center facility engineering teams on how they can improve PUE performance. The predictive PUE model is 99.6% accurate. Jim and Joe discussed how Google looked for a high degree of confidence in order to trust the numbers, and the human operators are an important part of the process like UPS drivers on their route. UPS is famous for creating better routes for its drivers, but I bet they were not even close to the % savings Google achieved.

So how good are the results? Google achieved from 8% to 25% reduction in its energy used to cool the data center with an average of 15%. Who wouldn’t be excited to save an average of 15% on their cooling energy costs by providing new settings to run the mechanical plant? Below is an example of what was historical PUE (blue) and New PUE (green) for a site.

One of the risks Google took in this presentation is they let a 25 year old mechanical engineer get on stage. Was the risk of the kid presenting? No, Jim was as polished as many who have presented for years. The risk was everyone at 7x24 Exchange knew who Jim was and they could try and see if he would consider leaving Google. :-)

The idea of using Machine Learning in data centers is new and have shown what can be discovered in the data. It’s like there was a hidden story there waiting to be told. Does you data center staff look for hidden stories in the data? Shouldn’t you if you can save between 8-25% of the energy in systems.