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How AI is Transforming the Data Center

Level 11

There have been so many changes in data center technology in the past 10 years, it’s hard to keep up at times. We’ve gone from a traditional server/storage/networking stack with individual components, to a hyperconverged infrastructure (HCI) where it’s all in one box. The data center is more software-defined today than it ever has been with networking, storage, and compute being abstracted from the hardware. On top of all the change, we’re now seeing the rise of artificial intelligence (AI) and machine learning. There are so many advantages to using AI and machine learning in the data center. Let’s look at ways this technology is transforming the data center.

Storage Optimization

Storage is a major component of the data center. Having efficient storage is of the utmost importance. So many things can go wrong with storage, especially in the case of large storage arrays. Racks full of disk shelves with hundreds of disks, of both the fast and slow variety, fill data centers. What happens when a disk fails? The administrator gets an alert and has to order a new disk, pull the old one out, and replace it with the new disk when it arrives. AI uses analytics to predict workload needs and possible storage issues by collecting large amounts of raw data and finding trends in the usage. AI also helps with budgetary concerns. By analyzing disk performance and capacity, AI can help administrators see how the current configuration performs and order more storage if it sees a trend in growth.

Fast Workload Learning

Capacity planning is an important part of building and maintaining a data center. Fortunately, with technology like HCI being used today, scaling out as the workload demands is a simpler process than it used to be with traditional infrastructure. AI and machine learning capture workload data from applications and use it to analyze the impact of future use. Having a technology aid in predicting the demand of your workloads can be beneficial in avoiding downtime or loss of service for the application user. This is especially important in the process of building a new data center or stack for a new application. The analytics AI provides helps to see the entire needs of the data center, from cooling to power and space.

Less Administrative Overhead

The new word I love to pair with artificial intelligence and machine learning is “autonomy.” AI works on its own to analyze large amounts of data and find trends and create performance baselines in data centers. In some cases, certain types of data center activities such as power and cooling can use AI to analyze power loads and environmental variables to adjust cooling. This is done autonomously (love that word!) and used to adjust tasks on the fly and keep performance at a high level. In a traditional setting, you’d need several different tools and administrators or NOC staff to handle the analysis and monitoring.

Embrace AI and Put it to Work

The days of AI and machine learning being a scary and unknown thing are past. Take stock of your current data center technology and decide whether AI and/or machine learning would be of value to your project. Another common concern is AI replacing lots of jobs soon, and while it’s partially true, it isn’t something to fear. It’s time to embrace the benefits of AI and use it to enhance the current jobs in the data center instead of fearing it and missing out on the improvements it can bring.

9 Comments
MVP
MVP

Nice write up, thank you.

MVP
MVP

It's all getting very clever

Level 13

Thanks for the article!

Level 11

is a great tool for development but I am not sure it's "ready for prime-time". data centers are using more and more resources in our world and more and more end users become heavy end users and demand more from our networking environment.  AI can fine tune or needs but initially human intervention will ne needed and human oversight will always be a must.  I would like to hear a lot more on this

Level 13

Thanks for the post.  Good write up.  I totally agree that we're heading in the right direction, having things on autopilot can free you up to do more important tasks, but the danger is (just like in the modern aircraft cockpit) is that you become so dependent on the automation that you never develop the tools to do it on your own or they get rusty from disuse.  You also have to watch the automation to make sure that the decisions/choices it's making are smart.  Everything in AI/ML is just an algorithm, and if the decision processing rules don't work well in your situation you probably won't get what you want.  There are also outside factors that can't even be incorporated into the processing.

MVP
MVP

I used to be wary of products and tools that performed tasks traditionally done by people.

But as my workload has increased I've become totally reliant on this kind of automation.

From the alert and monitoring point of view it has become much less useful to get an urgent alert for high cpu from an application server than it is to receive an email (that you read the following morning) which tells you that your application auto-scaled to meet demand.

Level 13

Thanks for the article

Level 12

I look forward to effective AI usage in load balancing. I've done some experiments with distributed computing that had less than desirable results. Either too many processes had data on one node and the processes were all run there, or the process of moving a lot of small files across a network slowed down processing on another node. An ability to anticipate this situation and move data to a less-used node in advance of work would be wonderful. In more practical situations, this would help with data-intensive web applications.

Level 12

hi i work in a small data center and this article is very interesting, thanks for posting it.