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I can't believe this one passed under my radar: https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ https://deepmind.com/blog/dee
by rattray 10y ago
I can't believe this one passed under my radar:
https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ https://deepmind.com/blog/deepmind-ai-reduces-google-data-ce...
> Our partnership with Google’s data centre team used AlphaGo-like techniques to discover creative new methods of managing cooling, leading to a remarkable 15% improvement in the buildings’ energy efficiency.
Which itself is an understatement of the achievement:
> Our machine learning system was able to consistently achieve a 40 percent reduction in the amount of energy used for cooling, which equates to a 15 percent reduction in overall PUE overhead after accounting for electrical losses and other non-cooling inefficiencies. It also produced the lowest PUE the site had ever seen.
Basically, they built an AI that was able to tune the "large industrial equipment such as pumps, chillers and cooling towers" to react to the dynamic, nonlinear interactions that vary within and between datacenters (weather, utilization, etc).
They also describe this AI as "General":
> Because the algorithm is a general-purpose framework to understand complex dynamics, we plan to apply this to other challenges in the data centre environment and beyond in the coming months.
They seem to imply that this technique could make almost any industrial process more efficient with minimal oversight/training/customization.
- forgotmyhnacc 10y agoI remember reading the blog post. Did they ever follow up with a research paper explaining what they actually did?
- gwern 10y agoSadly, not yet. Might be too secret sauce even for Google.
- conistonwater 10y agoNonlinear control with neural networks has existed for a long time, though, it's not exactly new. Is there reason to think they did something profoundly new in that specific instance?
- mooneater 10y agoProbably the use of a DNC for industrial application is novel.
- gwern 10y agoYes. When this came first up, someone on /r/machinelearning scoffed that using NNs to control fans/coolers/power systems to optimize was trivial and obvious and had been done for decades. When I challenged him for a ref, he produced a PDF which listed dozens of industrial applications for various nonlinear NN algorithms... none of which were actually autonomously controlling anything and most were predictive.
- haimez 10y agoLol, no. It's just the scale of the use that makes it valuable. Google datacenter cooling problems only exist inside of Google data centers, and the control knobs therein are 1. measurable AND measured 2. deterministic 3. simple. They're innovating in a problem space that only exists at Google, Microsoft, and Amazon. This is the boringest of sauces.
- deepnotderp 10y agoIt's not secret sauce, the algos are well known, read carefully, the guy who proposed it was an ML outsider. And besides, they stated they plan to release it.
- mooneater 10y agoTheir past paper on this: https://www.google.com/about/datacenters/efficiency/internal/assets/machine-learning-applicationsfor-datacenter-optimization-finalv2.pdf https://www.google.com/about/datacenters/efficiency/internal... In the blog post in July 20 2016 they said "We are planning to roll out this system more broadly and will share how we did it in an upcoming publication" https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ https://deepmind.com/blog/deepmind-ai-reduces-google-data-ce...
- deleted 10y ago[deleted]
- ehsankia 10y agoAnd you gotta realize that given the massive power usage at Google, this is huge, both for the environment and for Google's financials. In 2014, their data centers used 4,402,836 MWh. Even at 25$ per MWh, that's already $100B, so this little project basically saved them around $40B...
- mda 10y agoIts 100 millon not 100 billion