While IT leaders are applying SD-WAN, secure access service edge (SASE), and artificial intelligence (AI) with the aim of enabling the remote workforce, they could also be laying the building blocks for an autonomous network. Here is how to leverage investments made today for the autonomous networks of tomorrow.
With organizations working virtually through 2021 and even permanently, business continuity is more important than ever. But making the transition to remote work with more cloud applications and digital business transactions often requires IT modernization and investments addressing SD-WAN, SASE, and AI.
It’s important to see the larger transformative impact of SD-WAN and AI investments because together, they can serve as a springboard for the autonomous networks of the future. I predict that by 2025, 40% of network management tasks will be automated, reducing human errors and generating efficiencies that will decrease network outages by 50%.
The urgency for SD-WAN, SASE, and AI-powered automation has increased because these tools have been shown to improve cloud application performance for remote workers while also addressing the challenges of manual network analysis and security management. Consider that:
Forward-leaning IT leaders are connecting the dots between the immediate needs of their work-from-anywhere business and their long-term transformation strategies. They understand how SD-WAN, SASE, and AI come together in a step-by-step plan to build an autonomous network that is self-aware, self-managing, and ultimately “self-driving.”
When there is more emphasis on perfect VoIP, video calls, and creating a reliable on-ramp to cloud services, autonomous networks help IT teams deliver the results executives want without getting bogged down in the day-to-day operations. Business leaders, in turn, spend less time worrying about the network performance and have more time for strategic initiatives, thus accelerating the pace of digital transformation. All in all, COVID-19 has made autonomous networking now a need-to-have.
Autonomous networks are often thought of as still years away, but it’s all within reach. The tools needed to build them are already here and are rising on the list of IT priorities today. It’s only a matter of connecting the right pieces and training the system.
Readiness is a key factor for autonomy. AI tools need direct access to the network’s raw data streams and control systems, so it can collect and push information through an analytics engine. The simpler it is to connect the network and the AI engine (and the more data sources you feed it), the more intelligent your autonomous network will be. The complexity of the underlying IT architecture can complicate AI analysis and automation, as not every legacy network will be fit for AI.
Having one transparent ecosystem built on a common software-defined architecture will provide the clearest visibility from edge to edge, and those already making investments in SD-WAN, SD-networks, and SASE will be better prepared. SD-WAN and SASE are the first step in the path to an autonomous network because they establish a software-defined control plane and agile IT infrastructure where security is integrated. More importantly, their centralized console allows AI tools to both gain access to the real-time information they need and also make any necessary changes–all in one place. Plus, they help with quick-deploy cloud security and micro-segmentation capabilities.
As the AI-based analytics engine, AI for IT operations (AIOps) technologies are the other core component of any autonomous network. AIOps is expected to revolutionize IT operations because it empowers organizations to move from data-driven decision making to machine-assisted decision making.
AIOps acts as a 24/7 virtual network assistant, offering up recommendations to solve network performance issues and helping IT not just identify problems but predict and prevent them. How does it do that? Machine learning, behavioral analytics, and predictive analytics establish a baseline of network behavior, recognizing bandwidth demand spikes, patterns, anomalies, relationships, as well as root cause correlation. And, they keep analyzing the environment, establishing a dynamic picture of “normal.” AIOps can be purchased as a standalone solution that requires integration. Or, it may come as an embedded toolset in the SD-WAN or SASE solution. Embedded solutions deliver a key advantage–network performance analytics and AIOps insights will be unified in a single dashboard, helping reduce IT complexity.
The key to reaching the milestone of full autonomy is when AIOps tools advance beyond being only network advisors and are given the ability to make changes to the network. Again, this is where SD-WAN comes into play. When AIOps is already embedded into the SD-WAN controller, it already has direct access to drive configuration changes on its own. Now, the only thing left is to train the system so you can trust it to act alone on the recommendations it makes.
Playbooks are the guide rails for the autonomous network. AIOps needs a set of rules that directly align with business outcomes, giving it the context, it needs to make a meaningful impact. Principal Research Analyst at Nemertes Research, John Burke, describes this as “an iterative process of learning, feedback, and adaptation, a close collaboration between staff and software that transfers knowledge (both ways, ultimately) of network characteristics and performance as well as network context and meaning.” Coaching and trust take time, so getting an early start will drive toward total autonomy faster.
The idea of a self-driving network can be intimidating, and many leaders are fearful of automation and causing job losses with the displacement of network engineers. But the human brain is inefficient at the high-volume, complex network analysis that is necessary to compete in today’s fast-moving business landscape. AI and machine learning bring great benefits in eliminating human errors, which are still the root cause of most network outages today. Automated validation avoids these mistakes, addressing error conditions more efficiently than any human can.
As I look at the future, there will undoubtedly be changes in the world that require more rapid-fire response from businesses and their IT teams. Moving at digital speed and at scale will not be possible without an autonomous network. AI-based automation is here, and it won’t be long before technology will lift IT teams out of their administrative roles and elevate them into more strategic work. IT executives working through the challenges of remote work are likely already halfway there.
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