Machine Learning and the Future of ITOps
ITOps – In conjunction with the launch of its new Digital Experience Insights (DXI) platform, CA invited me to sit in on the ITOps Analytics 2.0 Summit that they held last week. While ITOA & monitoring isn’t my core focus, when I dug into what they were doing, I found a few of areas of overlap that piqued my interest, namely the machine learning, big data, open source and multi-cloud angles.
I spend the bulk of my time now tracking the machine learning & AI space, but coming from a focus on cloud for many years — and from the telecom industry many years before that — I’m keenly interested in how these technologies can be used to facilitate the delivery of applications as well as data center and network services. There’s a ton of opportunity in this space that we’re just starting to see come to fruition.
One poster-child case study that illustrates the opportunity at the intersection of ITOps and AI comes from Google. Over the past few years a collaboration between a research engineer from their DeepMind AI unit and one of their data center engineers resulted in the development of a machine learning system that could take as input a variety of data center operating factors such as workload characteristics, environmental variables, and equipment operating conditions and determine the ideal operating configuration for data center power and cooling equipment. A year ago the company announced that these neural network based models had been put into a production DC and resulted in a 40% reduction in energy consumption!
This article originally appeared on medium.com To read the full article, click here.
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