The Rise of AI-Powered Data Centers

By Nathan Sykes

The ideal data center is one that can run itself while remaining efficient and reliable. Luckily, many of the processes that take place inside a data center are already set up to be autonomous — from cooling and networking to computing and data storage.

Of course, to some degree, there’s always someone waiting in the wings to ensure everything runs smoothly. This fact is especially true for internal data centers, housed on the property of a major business or organization.

Artificial Intelligence and machine learning have quite a bit to offer in this regard, and will eventually power the data centers of the future. Eliminating the necessity for that watchful eye is the end goal for many players in the industry. With a recent announcement from Hewlett Packard Enterprise, we’re one step closer to seeing this happen.

According to HPE, they’ve established an add-on that will predict a variety of common troubles and put an end to them before they even start. Aptly titled InfoSight AI, it will make use of an AI-powered recommendation engine to analyze and detect various patterns. Their tool is not going to be available for some time, but it’s definitely a step in the right direction.

What Does a Data Center of the Future Look Like?

A Gartner report predicts that by 2020, 30 percent of data centers that have failed to incorporate AI and machine learning will cease to be “operationally and economically viable.” In other words, data center providers have no choice but to include these technologies if they want to continue operating far into the future.

But what does that look like? How does the future data center run? What kind of features and functionality set it apart from previous generations?

Automated systems — powered by AI and Big Data — will be able to self-manage data centers soon. Not just that, they will be able to maintain them as well, which means repairing or dealing with even the most severe problems. The idea is to create a well-designed and efficient system that can eliminate or mitigate downtimes completely.

As our reliance on modern technology grows, we’ll need more access to edge computing and cloud computing platforms. The very livelihood of our economy and enterprise markets will come to rely on the reliability and uptime of data centers and remote technologies. Therefore, downtimes are unacceptable, which is what AI and machine learning tools will help lessen.

Of course, it’s not just about performance. Cybersecurity is also a growing concern for any party involved with data and open connections. The AI systems powering future data centers will also be capable of handling and maintaining security measures and protections. Think about monitoring and analytics tools that can track network activity and various users. If the system identifies something strange, it can immediately lock out access to a specific user, confining their access to a limited area or taking it away altogether. This would help eliminate threats and reduce the risk of attacks.

To make any of this happen, however, developers will need to craft the necessary software and algorithmic tools that will serve as resources to AI and machine learning systems. Those same platforms must be trained through rigorous testing procedures and an extensive supply of historical data. Neural networks are the obvious choice for overseeing the training and growth of such platforms.

With any luck, the basic concept of an AI or voice-enabled assistant such as Alexa or Siri will become the foundation for new AI platforms and technologies. While it’s possible, this will have no direct correlation with the future of data centers and modern computing, it’s unlikely the gap won’t be bridged in some way.

AI-powered systems will need to be monitored and controlled in some way. It’s not a stretch to believe data administrators and scientists will be able to interact with such systems through voice commands and gestures.

For now, it’s important just to understand and accept the fact that modern AI and machine learning, as we know it, will be responsible for changing the face of the average data center.

About the Author

Nathan Sykes writes about business technologies on his blog, Finding an Outlet. To read his latest articles, follow him on Twitter @nathansykestech.

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