From smartphone assistants to image recognition and translation, machine learning already helps us in our everyday lives. But it can also help us to tackle some of the world’s most challenging physical problems, such as energy consumption.
Large-scale commercial and industrial systems like data centers consume a lot of energy, and while much has been done to stem the growth of energy use, there remains a lot more to do given the world’s increasing need for computing power.
Reducing energy usage has been a major focus for DeepMind over the past 10 years: we have built our own super-efficient servers at Google, invented more efficient ways to cool our data centers and invested heavily in green energy sources, with the goal of being powered 100 percent by renewable energy. Compared to five years ago, we now get around 3.5 times the computing power out of the same amount of energy, and we continue to make many improvements each year.
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Major breakthroughs, however, are few and far between, which is why we are excited to share that by applying DeepMind’s machine learning to our own Google data centers, we’ve managed to reduce the amount of energy we use for cooling by up to 40 percent. In any large-scale energy-consuming environment, this would be a huge improvement. Given how sophisticated Google’s data centers are already, it’s a phenomenal step forward.
The implications are significant for Google’s data centers, given its potential to greatly improve energy efficiency and reduce emissions overall. This will also help other companies who run on Google’s cloud to improve their own energy efficiency. While Google is only one of many data center operators in the world, many are not powered by renewable energy. Every improvement in data center efficiency reduces total emissions into our environment and with technology like DeepMind’s, we can use machine learning to consume less energy and help address one of the biggest challenges of all – climate change.
One of the primary sources of energy use in the data center environment is cooling. Just as your laptop generates a lot of heat, our data centers (which contain servers powering Google Search, Gmail, YouTube, etc.) also generate a lot of heat that must be removed to keep the servers running. This cooling is typically accomplished via large industrial equipment such as pumps, chillers and cooling towers. However, dynamic environments like data centers make it difficult to operate optimally for several reasons:
The equipment, how we operate that equipment, and the environment interact with each other in complex, nonlinear ways. Traditional formula-based engineering and human intuition often do not capture these interactions. The system cannot adapt quickly to internal or external changes (like the weather). This is because we cannot come up with rules and heuristics for every operating scenario. Each data center has a unique architecture and environment. A custom-tuned model for one system may not be applicable to another. Therefore, a general intelligence framework is needed to understand the data center’s interactions.
Because the algorithm is a general-purpose framework to understand complex dynamics, we plan to apply this to other challenges in the data center environment and beyond in the coming months. Possible applications of this technology include improving power plant conversion efficiency (getting more energy from the same unit of input), reducing semiconductor manufacturing energy and water usage, or helping manufacturing facilities increase throughput. We are planning to roll out this system more broadly so that other data center and industrial system operators (and ultimately the environment) can benefit from this major step forward.
By Richard Evans, Research Engineer, DeepMind