Google made headlines when it revealed that it is using machine learning to optimize its data center performance. But the search giant isn’t the first company to harness artificial intelligence to fine-tune its server infrastructure. In fact, Google’s effort is only the latest in a series of initiatives to create an electronic “data center brain” that can analyze IT infrastructure.
Automation has always been a priority for data center managers, and has become more important as facilities have become more complex. The DevOps movement seeks to “automate all the things” in a data center, while the push for greater efficiency has driven the development of smarter cooling systems.
Where is this all headed? Don’t worry. The data center won’t be a portal to Skynet anytime soon. Data center managers love technology, but they don’t totally trust it.
“You still need humans to make good judgments about these things,” said Joe Kava, vice president for data centers at Google. “I still want our engineers to review the recommendations.”
Kava said last week that Google has begun using a neural network to analyze the oceans of data it collects about its server farms and to recommend ways to improve them. Kava said the use of machine learning will allow Google to reach new frontiers in efficiency in its data centers, moving beyond what its engineers can see and analyze.
While there have been modest efforts to create unmanned “lights out” data centers, these are typically facilities being managed through remote monitoring, with humans rather than machines making the decisions. Meanwhile, Google and other companies developing machine learning tools for the data center say the endgame is using artificial intelligence to help design better data centers, not to replace the humans running them.
Romonet: predictive TCO modeling
One company that has welcomed the attention around Google’s announcement is Romonet, the UK-based maker of data center management tools. In 2010 the company introduced Prognose, a software program that uses machine learning to build predictive models for data center operations.
Romonet focuses on modeling the total cost of ownership (TCO) of operating the entire data center, rather than a single metric such as PUE (Power Usage Effectiveness), which is where Google is targeting its efforts. The company says its predictive model is calibrated to 97 percent accuracy across a year of operations.
Google’s approach is “a clever way (albeit a source-data-intensive one) of basically doing what we are doing,” Romonet CEO and co-founder Zahl Limbuwala wrote in a blog post. “Joe’s presentation could have been one of ours. They’ve put their method into the public domain but not their actual software – so if you want what they’ve got you need to build it yourself. Thus they just shone a light on us that we couldn’t have done ourselves.”
Romonet’s modeling software allows businesses to accurately predict and manage financial risk within their data center or cloud computing environment. Its tools can work from design and engineering documents for a data center to build a simulation of how the facility will operate. Working from engineering documents allows Romonet to provide a detailed operational analysis without the need for thermal sensors, airflow monitoring or any agents – which also allows it to analyze a working facility without impacting its operations.
These types of models can be used to run design simulations, allowing companies to conduct virtual test-drives of new designs and understand how they will impact the facility.
“I can envision using this during the data center design cycle,” said Google’s Kava. “You can use it as a forward-looking tool to test design changes and innovations.”
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