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WAN Automation: Why SD-WAN is Only Step One and What’s Needed to Reach Full Autonomy

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Network management automation
(Source: Pixabay)

Much like self-driving vehicles, IT networks are well on their way to becoming self-healing and self-optimizing, serving up the right bandwidth at the right time for perfect cloud application performance. At the source of these network breakthroughs is artificial intelligence (AI). AI-based automation technologies will soon transform network management and application performance as we know it, making them an opportunity that cannot be missed.

But before any IT professional can become a strategic leader in this new arena, they must first understand the current state of networking automation, the steps that IT teams need to make in order to arrive at full autonomy, and the truth about what today’s solutions deliver--not to mention how they differ. Here’s what you need to know.

SD-WAN Alone is Only the First Step in WAN Automation

Software-defined principles serve as only the first steps in creating a self-driving network. The path to complete autonomy starts with virtualization, and it is here that SD-WAN has made its mark. SD-WAN shifts the network control plane from hardware to software, allowing IT teams to stop manually managing devices and start leveraging programmable functions for WAN management. But SD-WAN is only a partial formula. In fact, it’s what you do after the SD-WAN implementation that can make a far bigger impact on network efficiency and automation. Consider that SD-WAN solutions provide visibility into performance and traffic, but IT teams often revert back to manual processes the minute they need to act on this intelligence. It is here where the next biggest automation opportunity lies.

AIOps: Your Guide to Autonomous Networking

The next step in creating autonomy is to apply AI-based technologies, where behavior analytics and machine learning algorithms can more fully automate network management and application optimization. Gartner refers to this concept as “AIOps,” defining AI for IT operations as the application of machine learning and data science to IT operations problems.

Analysts believe the long-term impact of AIOps will be transformative for IT teams. According to Gartner, “organizations that automate more than 70% of their network change activities will reduce the number of outages by at least 50% and deliver services to their business constituents 50% faster.” With payouts like that, it’s no surprise that analyst Andrew Lerner advises IT executives to prioritize network automation investments.

Applying AIOps to Build a Self-Driving Network

AIOps platforms have the power to act as virtual assistants or network engineer “robots” that work 24/7, never sleep, and can ingest and analyze big data at nearly the speed of light. Aggregating real-time network activity, historical traffic, configuration settings, and usage, AIOps platforms generate contextual intelligence and help eliminate human errors that are still the root cause of most service degradations and outages today.

Through behavioral analysis and machine learning, AIOps can identify patterns, processes, and trends, making predictions about bandwidth needs and offering recommendations to solve age-old IT problems. For instance, AIOps can recommend:

  • Which path an application should take, based upon performance

  • When and where to add bandwidth, including cloud vendors

  • Network changes and configuration settings to optimize application performance, based on business needs and service priorities

With coaching, feedback, and maturity, AIOps solutions then reach a critical milestone that allows trust to develop. Using the known rule sets, policies, and playbooks that govern in unmanaged environments that’s when an AIOps tool can become truly autonomous--trusted to act alone. With the right integration and automation tools, the AIOps system can be used to make adjustments to the network itself.

AIOps Solutions: Features that Make a Difference

First, it is important to note that not all IT infrastructures are designed for AI innovation. The underlying network must have a software-defined architectural model that supports real-time flexibility, big data collection, and secure analytics at speed and at scale. Networks that were architected for a previous era will likely never be optimized to support AIOps.

Mature AIOps platforms can make all the difference. Advanced solutions and providers with a library of specialization in network optimization best practices are better suited to create rulesets that can move quickly beyond the initial steps of data aggregation and analysis with the ability to act on recommendations autonomously.

The problem with even mature AIOps solutions today is that they are largely point solutions with their own management portals. This means customers get AI analytics and recommendations in a separate portal that must be integrated into their network and IT environment. Instead, enterprises need a holistic approach that puts all analytics in one place.

The key for technology partners is to ensure their offerings bring AI maturity with years of experience and are seamlessly integrated to avoid the complexities of management. For IT teams, these aspects will amplify the sense of true freedom that network automation is poised to deliver.