Google Lowers Data Center Energy Use through Machine Learning
Google recently released a white paper on how it is optimizing its data center operations through the use of machine learning. The practice is helping the company drive down its energy use.
According to a May 28 Google blog post, the machine-learning model is the brainchild of Jim Gao, an engineer on the company’s data center team. Although Google said its data centers use half the energy of typical data centers, Gao wanted to develop a model that would predict and improve data center performance.
The model works similarly to speech recognition software. It runs variables such as IT load and outside air temperature through a computer, and the computer analyzes the data to find patterns and “learn” from them. Google engineers then use this data to see how these variables interact with one another so they can develop new ways to make data centers operate more efficiently.
Google said the models are 99.6 percent accurate in predicting power usage effectiveness (PUE).
Image credit: A simplified version illustrating how the model works. Image via Google.
- Get Smarter About Your Energy Procurement Data Book
- NAEM Research Report: Planning for a Sustainable Future
- Verdantix Green Quadrant for EHS Software
- 2015 Environmental Leader Product & Project Awards
- Unlocking the Value of Energy & Operational Data
- Improve Your Company's Environment and Energy Performance
- Migration to Mobile: The Evolution of EHS Management Tools
- Gartner Magic Quadrant
- 2013-2014 Winter Polar Vortex
- Increase the Value of Demand Response Through Automation