Data Center With a Brain: Google Using Machine Learning In Server Farms 26
1sockchuck (826398) writes "Google has begun using machine learning and artificial intelligence to analyze the oceans of data it collects about its server farms and recommend ways to improve them. Google data center executive Joe Kava said the use of neural networks will allow Google to reach new frontiers in efficiency in its server farms, moving beyond what its engineers can see and analyze. Google's data centers aren't yet ready to drive themselves. But the new tools have been able to predict Google's data center performance with 99.96 percent accuracy."
Modeling outpaces practical feedback (Score:4, Informative)
Artificial intelligence and neural networks are a hot topic, so this is piggy-backing on that trend. It's not a surprise that Andrew Ng's work is referenced quite a bit.
While the modeling is interesting, it's seems to be just modeling at this point. The main claim of the white paper is high PUE prediction accuracy by the model. While that's academically interesting, the real use is in feedback for optimization. The white paper author realized that and included that optimization problem as one of the examples in the paper. However, the optimization was achieved "through a combination of PUE simulations and local expertise." I'm guessing that the local expertise part was relatively significant because there is basically no discussion of this even though it is the one application that would really make this work practical and really interesting. The paper claimed that this neural network-based optimization reduced PUE "by ~0.02 compared to the previous configuration." But, I have no idea how that would have compared to optimization using just local expertise without the benefit of neural network modeling.
Re:Neural Network? (Score:3, Informative)
Not outdated - just that they work well only with certain use cases. I know that SMART whiteboards use neural networks to process camera input in identifying if a finger or a pen is being used to write something - and they are very good at that. No matter how you hold the the pen they will recognise it, and never confuse a finger for a pen. They are fussy enough that I've been unable to duplicate a pen for myself with a 3D printer.
But they are no miracle solution. There are plenty of cases where you might think a neural network would work, yet it fails dismally. The research in recent years has been into the properties of much, much larger networks - something made possible by access to a lot more processing power. It hasn't been terribly promising. They are unpredictable.