Back in 1993, ASHRAE organized a competition called the “Great Energy Predictor Shootout,” a competition designed to evaluate various analytical methods used to predict energy usage in buildings. Five of the top six entries used artificial neural networks. ASHRAE organized a second energy predictor shootout in 1994, and this time the winners included a balance of neural networks and non-linear regression approaches to prediction and machine learning. And yet, as successful as the case studies were, there was little to no adoption of this compelling technology.
Fast forward to 2014 when Google announced its use of machine learning leveraging neural networks to “optimize data center operations and drive…energy use to new lows.” Google uses neural networks to predict power usage effectiveness (PUE) as a function of exogenous variables such as outdoor temperature, and operating variables such as pump speed. Microsoft too has stepped up to endorse the significance of machine learning for more effective prediction analysis. Joseph Sirosh, corporate vice president at Microsoft, says: “traditional analysis lets you predict the future. Machine learning lets you change the future.” And this recent article advocates the use of predictive analytics for the power industry.
The Vigilent system also embraces this thinking, and uses machine learning as an integral part of its control software. Specifically, Vigilent uses continuous machine learning to ensure that predictions driving cooling control decisions remain accurate over time, even as conditions change (see my May 2013 blog for more details). Vigilent predictive analysis continually informs the software of the likely result of any particular control decision, which in turn allows the software to extinguish hot spots – and most effectively optimize cooling operations with desired parameters to the extent that data center design, layout and physical configuration will allow.
This is where additional analysis tools, such as Vigilent’s influence maps, become useful. The influence maps provide a current, real-time and highly visual display of which cooling units are cooling which parts of the data floor.
As an example, one of our customers saw that he had a hot spot in a particular area that hadn’t been automatically corrected by Vigilent. He reviewed his Vigilent influence map and saw that the three cooling units closest to the hot spot had little or no influence on the hot spot. The influence map showed that cooling units located much farther away were providing some cooling to the problem area. Armed with this information, he investigated the cooling infrastructure near the hot spot and found that dampers in the supply ductwork from the three closest units were closed. Opening them resolved the hot spot. The influence map provided insight that helped an experienced data center professional more quickly identify and resolve his problem and ensure high reliability of the data center.
Operating a data center without predictive analytics is like driving a car facing backwards. All you can see is where you’ve been and where you are right now. Driving a car facing backwards is dangerous. Why would anyone “drive” their data center in this way?
Predictive analytics are available, proven and endorsed by technology’s most respected organizations. This is a technology whose time has not only come, but is critical to the reliability of increasingly complex data center operations.