3 Use Cases for Machine Learning Within SD-WAN

Software-defined wide area networks, or SD-WAN’s, have become a common network management solution for multi-location enterprises working within a variety of different industries. Much of this change stems from the fact that, due to innovations such as the Internet of Things, the demands on wide-area networks are growing profusely. More and more connected devices, cloud migration and an increasing focus on automation means that WAN’s will need to become smarter and more agile to cope with challenges related to network resources and security. For these reasons, network administrators are inclined towards machine learning based technologies to assist them with various aspects of network management.


In this article, we’ll be looking at three use cases of machine learning within software-defined wide area networks and detailing how machine learning technologies can be beneficial to software defined WAN management. Let’s dive in right away.

WAN Optimization

WAN optimization is one of the most commonly put forward ways in machine learning could be applied to SD-WAN. In theory, machine learning would, perhaps using some form of topology optimization, be able to analyse network branch traffic and work out the most appropriate connection for traffic of its type available at the time. Machine learning algorithms could then also be used to automatically route the traffic down the selected connection. There are currently models of WAN optimization technologies being worked on that shift traffic while taking into account security requirements, costs, and the application’s attributes.

It is hoped that, as machine learning and WAN path optimization technologies are refined and improved, they will eventually lead to increasingly intelligent and automated traffic optimization becoming a standard within SD-WAN solutions. As the use of artificial intelligence becomes more widespread, utilizing machine learning to enhance the overall efficiency of IP network traffic routing will most likely become an everyday practice for early adopters of the technology. The use of machine learning algorithms to predict the best connections for network traffic based on the identification of the first packet transferred is also currently being explored by some companies.

Fault Prediction

Fault prediction using machine learning technologies within SD WAN works in much the same was as predictive maintenance would in, say, the manufacturing industry. For example, on the manufacturing floor, data collected from IoT devices is analysed to predict when tools or equipment may need maintenance or repairs. This data can then be used to schedule downtime that fits into the manufacturer’s schedule best. Using machine learning for fault prediction within SD-WAN works much the same way. Using AI software to predict network faults could be used to enable either the automatic deployment of a solution based on what kind of problem has been diagnosed or the notifying of network administrators who could then go about solving the issue.

Lanner's NCA-2510: Virtualization Optimized, High Throughput Appliance for Advanced SD-WAN and SD-Security
Lanner’s NCA-2510: Virtualization Optimized, High Throughput Appliance for Advanced SD-WAN and SD-Security

Learning models could also be built around known faults and vulnerabilities so as to give machine learning deployments a fundamental understanding to then build upon. Within machine learning fault prediction for SD-WAN, both artificial neural networks (ANN’s) and a form of many-valued logic known as fuzzy logic are being experimented with in order to improve predictions for use within a real-time environment. The hope here is that, eventually, through AI and machine learning technologies, SD-WAN fault prediction will become both a more streamlined and simplified process. As network demands grow and more technologies begin to connect, it appears reasonable to expect that the potential for faults will increase. In light of this, powerful fault prediction solutions will be needed and AI and machine learning seem to fit the bill.

Network Management

The most exciting aspect of machine learning within SD-WAN, at least from a network administrator’s point of view, is its potential for revolutionizing the way networks are managed. The idea of using artificial intelligence and machine learning to improve network management and security is not a new one, with the first mentioning of such ideas dating back as far as the 1980’s. However, until recently, the technologies that would enable this kind of network management have been lacking. Now, at the dawn of the Fourth Industrial Revolution, machine learning algorithms can automatically route network traffic, predict faults and schedule fixes, as well as leverage network and security resources based on their own analysis of where they are needed.

And this is only the start. Expect to see plenty of innovative and experimental software-defined network systems appearing over the next 12 months as machine learning, SD-WAN, and an assortment of hybrid systems look to streamline, optimize, and simplify network architectures. As the amount of data generated by a continuously increasing number of connected devices grows, network administrators will need to keep finding new and improved ways to inject more agility and intelligence into their SD-WAN’s. This is where artificial intelligence and machine learning within SD-WAN comes in.

As both software-defined wide-area networks and artificial intelligence and machine learning technologies evolve and mature together, their integration with one another could allow for the support of ever more powerful applications and services, while also pushing even more intelligence to the network edge. The Internet of Things, edge and cloud computing, and artificial intelligence will all become synonymous with the Fourth Industrial revolution, however, it could well be capable and prepared SD-WAN’s that become the backbone of future network and technological innovations.

3 Use Cases for Machine Learning Within SD-WAN was last modified: December 3rd, 2018 by Rick Spencer
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