With security threats on the rise, many network administrators are exploring how machine learning and artificial intelligence (AI) can automate threat assessment and response processes to mitigate the risk of data breach. Machine learning algorithms have been tracking credit card fraud for a few years now, so it’s not new technology that we’re discussing. In fact, the biggest change to network troubleshooting is that machine learning and AI are now getting baked into everything.

From network troubleshooting to network analysis of your hardware, here is how machine learning will help you streamline your data center for the future.


Machine Learning for Future Data Centers
Accelerate Your Network Troubleshooting

Cloud computing is killing the small data center. Having a couple of standard racks in the back of a mid-size IT provider has morphed into hyper-scale acreage with scalable solutions for the biggest enterprise. These mega-farms come replete with their own problems, including the digital and physical management of large deployments. True to form, cloud providers like AWS and Azure are leading this trend by scaling back on data engineers and replacing their physical presence with increased automation, security staff, and lower level teams to do manual labor.

For the past year or so, we’ve been reading Chicken Little-type articles about how technology is replacing the common worker, eliminating everything from factory jobs to fast food gigs. Ironically, the tech we love is beginning to help automate large data centers in ways that will definitely change how network administrators see their role. While it’s clear that machine learning holds the key to the more streamlined data center operations, it may also change the traditional network administrator role forever.

Let’s look more closely at how machine learning is going to change your network analysis process in the future.

WHAT IS Machine Learning Security?

A January 2017 article in Security Week points out what anyone who followed the DDoS attack on Dyn already knows: Cyber terrorists are turning machine learning algorithms against us. The article states in part, "... due to the shortage of security professionals and the general challenge of dealing with big data sets in security, it is not surprising that vulnerability remediation efforts are not keeping up with cyber adversaries."

Machine Learning Security
Machine Learning Security

McAfee Labs predictors for 2017 include a red flag to watch for more personalized phishing and malware campaigns, as cyber bullies use machine learning to analyze stolen data and cull personal details to use against us. While the big cloud providers have been fighting fire with fire for years, today’s tools must be designed not only to detect breach after the fact but also to disrupt these attacks as they hit the network.

Complicating the security landscape is the fact that network analysis can no longer monitor the endpoint, but must respond to threats at multiple levels: application, cloud, mobile, and other IoT devices. Network troubleshooting must include these silo systems that meld into the network sometimes at will, analyzing and remediating threats as they occur. The bigger the network, the more difficult network analysis becomes, which is the best argument we’ve heard yet for leveraging machine learning to mitigate the risk.

The truth is that the volume of security-related data is so large that we must use automation to help us manage and interpret it. That’s why it’s fairly clear that tomorrow’s network troubleshooting in the big data center will use machine learning to observe and learn from security strategies, evolving them across the architecture.


Data Center Knowledge points out what may be an obvious fact: larger data centers with fewer employees means reliance on the machines themselves to track failures in crucial hardware. Having a data center that stretches for miles turns network troubleshooting into a challenging problem when you have thousands of racks and tens of thousands of servers.

iot computer network
Monitoring from the Edge
IoT sensors have stolen an idea found in factory automation to monitor, collect data, analyze it via machine learning, and then make infrastructure adjustments.

Monitoring isn’t just about the servers and firewalls, of course, it’s the cooling mechanisms as well that require 24/7 vigilance. According to Data Center Knowledge, data center infrastructure management (DCIM) software is the latest in machine learning for our increasingly large data centers. Companies like Vigilent offer data centers automated IoT sensors that use machine learning to evaluate the relationship between cooling, power use, and the risk of failure. The technology uses prescriptive analytics to monitor data centers and alert you when a critical cooling infrastructure is about to decline. The machine learning algorithms are so smart it can alert and correct hot spots rack by rack. It can also help data centers become a little greener by network troubleshooting and making minute output adjustments to improve electric usage.

The New Human-AI-MACHINE LEARNING Partnerships

Machine learning can help streamline network troubleshooting, allowing data center managers a more automated and intelligent process. But neither of these examples is effective without a human partnership. IoT sensors cannot hear the sound of a fan nearing the end of its life or the drip drop of a leaking pipe.

That’s why data center managers will continue to leverage machine learning as the first line of defense in network troubleshooting. The ability to capture, collate, and monitor the huge volume of security data will enable our network analysis of even the largest infrastructures. Whether it’s monitoring and responding to a cyber-security breach or placing remote sensors to monitor hardware, machine learning algorithms and the tools they spawn are going to continue to improve DCIM in the future. Data center managers will no longer have to perform data crunching and analysis.

The future state of DCIM will likely become a hybrid of AI and human interaction. This new model will not make the human data center manager obsolete, but will instead allow them to adapt to evolving cyber threats while automating and streamlining tasks. Keeping in mind that these advances aren’t even in the toddler phase, it’s clear that DCIM is changing as machine learning is evolving into a more generally accepted practice across all industries.

As the many changes and potential changes permeate the network troubleshooting paradigm, it becomes vital to ensure networks are kept running. To that end, IT professionals need reliable solutions with accurate reporting. To learn more about ways to gain a holistic view into potential service performance problems, go here.