{"id":13284,"date":"2018-09-05T11:00:12","date_gmt":"2018-09-05T19:00:12","guid":{"rendered":"http:\/\/www.palada.net\/index.php\/2018\/09\/05\/news-7051\/"},"modified":"2018-09-05T11:00:12","modified_gmt":"2018-09-05T19:00:12","slug":"news-7051","status":"publish","type":"post","link":"http:\/\/www.palada.net\/index.php\/2018\/09\/05\/news-7051\/","title":{"rendered":"Top 2 Ways AI Will Change Your Data Center Forever"},"content":{"rendered":"

Credit to Author: Guest Blogger| Date: Wed, 05 Sep 2018 16:15:00 +0000<\/strong><\/p>\n

As a child I was always fascinated with patterns and in particular, their impact on people. \u00a0Whether it\u2019s the repetitive beat of a drum inspiring us to dance or our early learnt abilities to distinguish the key features of cat from dog, patterns are clearly essential for human beings, both to socialize and to navigate a complex world. With its ability to recognize very complex patterns in unstructured or semi-structured data, artificial intelligence (AI) is poised to change the data center in major ways. I explored some of the possibilities and the overall potential impact of AI in the data center during a session at Schneider Electric\u2019s recent Innovation Day<\/a> event for cloud and service providers.<\/p>\n

Artificial Intelligence: Then and Now<\/h2>\n

\"manAI has certainly been around for a long time but, until now, its application was more theoretical. Unless you happened to be operating a super computer, processors were too slow to create meaningful results. That meant the early applications were often restricted to users with unusual budgets \u2013 the military were early adopters of image recognition technology to spot camouflaged vehicles in the huge quantities of aerial and satellite imagining that took off in the 1980s. GPU\u2019s changed all of that. Suddenly anyone could run complex machine learning models on consumer grade PC components.<\/p>\n

At its heart, AI is enabling us to solve problems; or at least, provide approximate answers, to hard problems that can\u2019t, or can\u2019t easily, be solved by traditional modeling or mathematical techniques. At Equinix<\/a>, we\u2019re already applying AI to our data centers.<\/p>\n

1. Data-driven Models for Data Center Optimization<\/h2>\n

Optimizing data centers is hard. IT loads and external ambient conditions vary dynamically and sometimes quite unpredictably; and of course, as operators and engineers we expend considerable effort during design to model, optimize and improve.<\/p>\n

If you want to materially improve the efficiency of an operational datacenter, you have limited options. You can build a detailed physics model and run \u2018what-if\u2019 simulations; but with so many variables, and the usual limitations on the depth of the model, it\u2019s hard to derive meaningful insight. Plus, what you build for one facility, may not be particularly useful in helping to understand the performance of others.<\/p>\n

By contrast, the AI approach \u2013 which in this context is really a data approach \u2013 takes the data and trains a network such that it is able to model the PUE of your facility. The inputs to your model are the many data points that you collect from your infrastructure and your \u2018test function.\u2019 \u00a0What you use to prove whether the network is accurately modelling the real-world performance of your facility, is the delta between predicted and measured PUE. Once you have a model that\u2019s capable of reliably predicting your PUE, you can tweak the input variables to simulate \u2018what-if\u2019 scenarios. \u00a0 For example: What happens if a particular pump is turned on (or off)? What would the impact be if the volume or temperature of chilled water was increased? What if the set point for CRAC units is raised? Expert engineers then make decisions derived from the data model.<\/p>\n

Beyond this, once there\u2019s trust in the data and confidence in the models, the next step is taking humans out of the equation and letting AI dynamically optimize the data center in real time. Embedding AI for full data center automation will increase efficiency even more moving to a \u2018fly-by-wire\u2019 style model of datacenter.<\/p>\n

2. Pattern-based Learning to Effectively Monitor Equipment<\/h2>\n

AI will also help with effective equipment monitoring. Many experienced datacenter engineers will tell stories of equipment that they could tell was faulty (or would shortly fault) because it sounded funny or it smelt different.<\/p>\n

We\u2019re testing our ability to augment those human capabilities to take data from our infrastructure \u2013 augmenting additional sensors if necessary and looking for patterns that ultimately translate into \u2018that device sounds funny\u2019 \u2013 well before that became detectable to people.<\/p>\n

The aerospace industry is a great template for how significant operational efficiencies can be made by replacing a planned service regime with a more flexible, data led program and Equinix is excited to develop our capabilities in this space, alongside our key partners such as Schneider Electric.<\/p>\n

Coming Soon to a Data Center Near You<\/h2>\n

AI is hard. There is a considerable skills gap, although it\u2019s improving all the time. For many organizations, issues around data (availability and quality) may limit what they can do. But the key is the data \u2013 whether you plan on applying AI now or in the future \u2013 if you don\u2019t have the data, and worse, if that data is no good, you\u2019ll struggle to build accurate AI models.<\/p>\n

AI is only going to get bigger \u2014 addressing more and more classes of \u201cunsolvable\u201d problems. In five years, data center equipment without some kind of embedded AI will be rare. For more on the future of data center efficiency, take a look at this session from Innovation Day. Kevin Brown, SVP of Innovation and CTO, IT Division, Schneider Electric talks about\u00a0where the next 80% of data center performance improvements will come from<\/a>.<\/p>\n

\n

About the Author:<\/h2>\n

David Hall, Senior Director of Technology Innovation, Equinix<\/h2>\n

\"DavidDavid has had an extensive career in the data center industry, spanning commercial, technical and innovation roles.<\/p>\n

At Telecity Group, he led the teams responsible for developing capacity and services for the hyperscalers – everything from edge networking to core compute deployments.\u00a0\u00a0 He was also responsible for creating the world\u2019s first private network to connect networks and enterprises to the public cloud.<\/p>\n

Now at Equinix, David is working to develop the next generation of Equinix data center design. Inspired by some of the technologies and approaches pioneered by the hyperscalers, he has a particular focus on extending the availability of those solutions to smaller service providers and enterprises while developing new solutions for challenges such as the evolution at the edge.<\/p>\n

David lives in London with his Old English Sheepdog, Oppenheimer.<\/p>\n<\/p><\/div>\n

The post Top 2 Ways AI Will Change Your Data Center Forever<\/a> appeared first on Schneider Electric Blog<\/a>.<\/p>\n

http:\/\/blog.schneider-electric.com\/feed\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Credit to Author: Guest Blogger| Date: Wed, 05 Sep 2018 16:15:00 +0000<\/strong><\/p>\n

As a child I was always fascinated with patterns and in particular, their impact on people. \u00a0Whether it\u2019s the repetitive beat of a drum inspiring us to dance or our… Read more »<\/a><\/p>\n

The post Top 2 Ways AI Will Change Your Data Center Forever<\/a> appeared first on Schneider Electric Blog<\/a>.<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"colormag_page_container_layout":"default_layout","colormag_page_sidebar_layout":"default_layout","footnotes":""},"categories":[12389,12388],"tags":[10245,11113,12390,12391],"class_list":["post-13284","post","type-post","status-publish","format-standard","hentry","category-scadaics","category-schneider","tag-ai","tag-artificial-intelligence","tag-colocation","tag-data-center"],"_links":{"self":[{"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/posts\/13284","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/comments?post=13284"}],"version-history":[{"count":0,"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/posts\/13284\/revisions"}],"wp:attachment":[{"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/media?parent=13284"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/categories?post=13284"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/tags?post=13284"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}