Machine Learning Tools are Coming to the Data Center
Back at the dawn of the internet, data centers could be small and simple. A large ecommerce service could do with a couple of 19-inch racks with all the necessary servers, storage, and networking. Today’s hyper-scale data centers cover acres, with tens of thousands of hardware boxes sitting in thousands of racks. Along with the design changes, these mega-server farms have been built in new, remote locations, trading proximity to large population centers for cheap power.
As they automate data center operations, public clouds like Amazon Web Services or Microsoft Azure hire fewer and fewer highly skilled data center engineers, who are usually outnumbered by security staff and relatively low-skilled workers who do manual labor, such as handling hardware deliveries. Fewer staff managing more servers means monitoring the power and cooling infrastructure requires greater reliance on sensors, which we might now call Internet of Things hardware. They help identify issues to an extent, but there are many cases in which the experience of a seasoned facilities engineer is hard to replace with sensors. These are things like recognizing a sound that indicates a fan is about to fail or locating a leak by hearing the sound of water drops.
You need more than sensors to monitor modern data center infrastructure, and a new generation of applications aims to fill the gap by applying machine learning to IoT sensor networks. The idea is to capture operator knowledge and turn it into rules to help interpret sounds and video, for example, adding a new layer of automated management for increasingly empty data centers. The services promise “to predict and prevent data center infrastructure incidents and failures,” Rhonda Ascierto of 451 Research told Data Center Knowledge. “Faster mean time to recovery and more effective capacity provisioning could also reduce risk.”
http://www.datacenterknowledge.com/archives/2017/07/05/machine-learning-tools-are-coming-to-the-data-center
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