SD-WAN is just the first step in WAN automation

Just like self-driving cars, IT networks are becoming self-healing and self-optimizing, delivering the right bandwidth at the right time for flawless cloud application performance. The source of these network breakthroughs is artificial intelligence (AI). We know that AI-based automation technologies will soon transform network management and application performance, making them an opportunity that cannot be missed.

But before any IT professional can become a strategic leader in this new space, they must first understand the current state of network automation, the steps IT teams need to take to achieve full autonomy, and the capabilities offered by today’s solutions.

SD-WAN is just the first step in WAN automation

Software-defined principles are only the first step in creating a self-driving network. The path to full autonomy begins with virtualization, and this is where SD-WAN shines. SD-WAN moves the network control plane from hardware to software, allowing IT teams to stop manually managing devices and start leveraging programmable capabilities for WAN management. But SD-WAN is only a partial formula.

What you’re actually doing is making a bigger impact on network efficiency and automation. Considering that SD-WAN solutions provide visibility into performance and traffic, IT teams often revert to manual processes the minute they need to take this intelligent action. After implementing SD-WAN, your next biggest automation opportunity is here.

AIOps: A Guide to Autonomous Networking

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

Analysts believe that the long-term impact of AIOps will be transformative for IT teams. According to Gartner, "Enterprises that automate more than 70% of network change activities will experience at least 50% fewer outages and deliver services to their business components 50% faster." With spending like that, it's no surprise that analyst Andrew Lerner recommends IT executives prioritize network automation investments.

Apply AIOps to build an automated network

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

Through behavioral analytics and machine learning, AIOps can identify patterns, processes, and trends, make predictions about bandwidth needs, and provide recommendations for resolving aging IT problems. For example, AIOps can suggest:

• Which path the application should take depends on performance.
• Add bandwidth when and where you want, including with cloud providers.
• Make network changes and configuration settings to optimize application performance based on business needs and service priorities.

Through assistance, feedback, and maturity, AIOps solutions reach a critical milestone that allows trust to develop. Using known rule sets, policies, and playbooks to manage in an unmanaged environment, this is when AIOps tools can become truly autonomous and act alone. With the right integration and automation tools, AIOps systems can be used to make adjustments to the network itself.

AIOps Solutions: What Makes a Difference

First, it is important to note that not all IT infrastructure is designed for AI innovation. The underlying network must have a software-defined architectural model to support real-time flexibility, big data collection, and fast, large-scale security analysis. Networks designed for previous eras may never be optimized to support AIOps.

A mature AIOps platform can make all the difference. Advanced solutions and providers with expertise in network optimization best practices are better suited to creating rule sets that can quickly move beyond the initial steps of data aggregation and analysis with the ability to autonomously act on recommendations.

The problem with even mature AIOps solutions today is that they are largely point solutions with their own management portals. This means that customers must 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.

For technology partners, the key is to ensure their products bring AI maturity with years of experience and integrate seamlessly to avoid management complexity. For IT teams, these aspects will amplify the true sense of freedom that network automation readiness brings.