Optimizing Data Centers Through Machine Learning
![Image](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi3Sgr-78Cqyl0LixivlETBzffe1OZ6LYtHqAlGqRc57rH4WLf7GqOTga3gSxwL6x1G5IYOmGOlBKWO1Fl_jXtrhQJxY_kNJJhsJIZhPWVrZcs_CL-Rl8G_0DwffsqoGKDyZw0dVQUV4ac/s1600/google_pue_infographic.png)
Google has published a paper outlining their approach on using machine learning, a neural network to be specific, to reduce energy consumption in their data centers. Joe Kava, VP, Data Centers at Google also has a blog post explaining the backfround and their approach. Google has one of the best data center designs in the industry and takes their PUE (power usage effectiveness) numbers quite seriously. I blogged about Google's approach to optimize PUE almost five years back! Google has come a long way and I hope they continue to publish such valuable information in public domain. There are a couple of key takeaways. In his presentation at Data Centers Europe 2014 Joe said: As for hardware, the machine learning doesn’t require unusual computing horsepower, according to Kava, who says it runs on a single server and could even work on a high-end desktop. This is a great example of a small data Big Data problem. This neural network is a supervised learning approach where you creat