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What You Need to Know About Machine Learning and Data Center Outages

By: Kaylie Gyarmathy on December 11, 2018

Machine learning is frequently used interchangeably with artificial intelligence, and while the two are similar, they’re not quite the same and have different implications for data centers. Artificial intelligence refers broadly to a machine’s ability to simulate human intelligence by performing human-like tasks while adjusting to new situations and stimuli. Machine learning is a subset of artificial intelligence research that focuses specifically on a computer’s ability to learn new tasks or to perform existing tasks more effectively on its own without human direction.

For data centers, machine learning presents a number of exciting opportunities. From power and cooling regulation to finely tuned network performance, these intelligent programs are already revolutionizing the way data centers are designed and operated. As the IT infrastructure of these facilities becomes more complex, machine learning will make it possible to keep vital systems running efficiently and avoid data center server outages that result in costly downtime.

Power and Cooling Optimization

The primary benefit of machine learning for data centers is its ability to manage power and cooling requirements more effectively. By monitoring data traffic over time, machine learning algorithms can build the optimal power usage schedule to ensure that the facility’s cooling system is ready to accommodate the additional heat generated by busy servers. This can significantly decrease operational costs for the data center, and the savings can be passed along to customers to make the facility more competitive.

Optimized systems are less likely to fall victim to unanticipated spikes in usage because the machine learning algorithms are able to anticipate when those spikes will occur. This helps a data center keep servers up and running more consistently, avoiding potentially costly downtime incidents.

Cloud Data

In order to learn more effectively, a machine learning program needs data. A lot of data. Every additional piece of information fed into its algorithms further refines the models that drive its learning capacity. As it incorporates more situations and contexts into its analysis, the program becomes more dynamic and responsive. It understands nuance and can better anticipate likely outcomes based on a composite of comparable events.

Data centers provide an idea environment for machine learning programs thanks to their connectivity options. With ready access to a multitude of cloud environments and other network resources, they can accumulate huge amounts of data to drive their learning algorithms and perform more effectively.

Incident Analysis

Machine learning programs can be set up to monitor any problems that occur within a data center’s complex infrastructure. When something goes wrong, the system can identify causes and take note of solutions that addressed the issue. Over time, this incident analysis allows automated systems to react to problems in real time or even anticipate them before they occur. In some cases, the system may be able to handle the problem completely on its own. If not, it can generate an incident ticket for a remote hands technician to resolve, oftentimes before the event has an opportunity to cause any real disruption. This can be tremendously helpful in avoiding routine problems that have the potential to cause downtime.

Predictive Maintenance

Just as ongoing incident analysis can help ensure that network software is operating at peak efficiency, predictive maintenance makes it possible to protect physical infrastructure from potential problems. By monitoring usage patterns and referencing extensive historical data on equipment usage, machine learning programs can generate an accurate forecast of when physical components in the facility will need to be replaced. It’s much easier to avoid a data center outage when repairs or replacements can be made according to a planned schedule rather than in the wake of an unexpected failure.


Cyberattacks have continued to increase in frequency over the last few years, but there is a bit of a silver lining in this unfortunate trend. More attacks means more data points for machine learning programs to take into consideration, which helps them to better optimize cybersecurity measures to protect data center networks from malware, ransomware, DDoS attacks, and other threats. Even when attacks on a facility do occur, machine learning programs will be able to analyze the attack afterwards and take steps to ensure that any exploited vulnerabilities are plugged to prevent future data center outages.

Autonomous Data Centers

For many companies, the ultimate goal of machine learning programs is to develop the systems that will finally make the fully autonomous data center a viable solution. While there are some facilities that operate with very limited human oversight, the concept of the unmanned hyperscale facility capable of delivering enterprise level services isn’t quite a reality just yet. Few businesses are quite ready to hand the whole of their data operations over to machines, although the industry could quickly shift to predominantly automated facilities with on-site remote hands technicians ready to provide support. This would eliminate many of the problems resulting from human error while still retaining the rapid response that only a remote hands team can provide in the event of data center outage.

Although machine learning has only begun to scratch the surface of its potential, it’s already making an impact on data center design and operation. More and more facilities are incorporating machine learning tools to drive costs down and deliver services more efficiently to minimize server downtime. As these programs continue to improve (often on their own!) In the future, data centers will get even better delivering high quality services that maximize uptime reliability.

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