Why Machine Learning (ML) and Artificial Intelligence (AI) Are Key Technologies for SD-WAN

Given the speed with which technology evolves to securely connect users to applications, coupled with the rapid changes in traffic patterns, SD-WAN is required to keep pace. New tools must constantly be developed and deployed to automatically detect and analyze anomalous network behavior before it affects end users. To mitigate and prevent network issues before they impact users, networking professionals must be quick to understand and adopt these tools.

One of the most valuable emerging technologies in networking, machine learning (ML) can save time for deploying changes, more effectively manage networking issues, and help to constantly and automatically adjust to new situations.

Artificial intelligence (AI) and machine learning are currently used in production for a broad range of use cases, including security. They are quickly being infused into IT operations and significantly changing the way humans interact with technology, providing a more proactive and automated approach.

Human errors are among the most common causes of unplanned network downtime. Automation eliminates human errors, but it doesn’t eliminate all the mistakes if humans still make the final decisions. In addition, troubleshooting wide area network (WAN) problems has historically been tedious and time-consuming, requiring administrators to sift through countless log files and alerts. The time needed to identify the source of network issues and solve them contributes significantly to a network’s total cost of ownership. Legacy software-defined WAN (SD-WAN) solutions may offer centralized network management, helping administrators locate issues more quickly, but ML-based capabilities found in next-generation SD-WAN can anticipate issues before they even happen. Today’s next-generation SD-WAN solutions with ML-based capabilities adapt to changes in your environment much more quickly than human intervention can.

When artificial intelligence and machine learning are embedded in an SD-WAN solution, the network gains massive data processing as well as a richer understanding of network and application performance. Basically, the network becomes smarter. The combination of automation, machine learning and artificial intelligence makes a self-healing, self-driving network – one that can monitor, analyze, correct and adjust with minimal human intervention. 

The Top 3 ML-Based Autonomous SD-WAN Capabilities

Next-generation SD-WAN solutions with ML-based capabilities allow you to simplify network operations and improve performance by providing three key benefits: 

• Accurate visibility into the network and deeper understanding of network and application performance. This is key to help resolve issues as they develop.
• Smart analytics, including intelligent alerts and recommendations for network changes after analyzing how different events affect the network, application performance and security.
• Automated statistical analysis to make capacity planning simple.

As SD-WAN continues to be widely adopted, it should expand the use of machine learning to all use cases in the future, shifting the focus from branch connectivity to automated operations. This will usher in a new era of expected exceptional performance, highly available and flexible bandwidth, and rich data and analytics, all while reducing operational costs.

To learn more about next-generation SD-WAN with ML-based capabilities, click here.

Additional Reading

• Behind the need for next-generation SD-WANs by ZK Research
• TEI Spotlight report shows SD-WAN customers' results