The Road to Autonomous Networks: Learning to Thrive in a Brave New World
In this story, our hero has learned to conquer the dreaded lock-in beast, and streamlined outdated processes and technologies to deliver new services, quickly and efficiently.
How much more quickly, you ask? In terms of building and delivering new services, processes that once took months to complete, are now accomplished in minutes.
Because our hero has created an abstracted network model that is able to retrieve an amazingly rich set of information and data from across the entire network infrastructure, he decides that it’s time to utilize this information to draw insights from the network. He decides to deposit and store this 'big data' in what is called a data lake, which can store data from both within and outside the network in an efficient, unstructured manner.
Because analytical insights and intelligence may be garnered from this data lake, our hero adds an analytical engine that can provide a more structured and normalized view of the data, complete with a set of rich APIs that support higher level applications. These APIs are designed to be open, to enable a wide range of future ideas, from multiple, varying sources, as and when they are created.
Blue Planet Analytics
Blue Planet Analytics generates deep network insights to help network operators make smarter, data-driven business decisions. Designed to help automate networks, this capability and related applications gives network operators the ability to visualize and identify trends that result in more profitable services, the ability to better predict capacity requirements, and anticipate potential network and service disruptions.
- Improved customer satisfaction – using performance-related data combined with machine learning algorithms, it now becomes possible to actually predict failures before they happen. By focusing on risk assessments, a network operator can stay ahead of potential outages. But just pointing to a problem won’t necessarily ensure the network availability customers demand, so evolving to a fully autonomous network requires that network intelligence evolves to automatically steer services away from potential, predicted problem areas.
- Improved demand planning – predicting capacity requirements and traffic growth was once a challenging, offline guessing game. By leveraging predictive analytics, it’s now possible to predict the specific areas that may require capacity augmentation, and to automatically trigger those changes in the network, as the operator’s network policy allows. Today, this process requires some human interaction and creative thinking, but in the future, this type of predictive planning will ultimately become the norm.
- Network optimization – as the rate of change accelerates in network operations, and more organizations enable both machine-driven and even external customer-driven inputs, the need for networks to self-optimize, in near real-time will ensure the network architecture remains ideally suited for the traffic types and services carried.
Each new application developed further increases the level of ‘intelligence’ in the network and provides much needed insights that our hero and other network operators can use to drive changes back into their now automated networking environments. Our hero’s new autonomous architecture continues to grow increasingly intelligent and autonomous, improving the capabilities of existing services and applications, and allowing his team of DevOps superheroes to create or rapidly deploy new applications that further enhance network operations.
Please understand—our hero’s journey isn’t meant to be definitive, nor will it provide all answers for everyone. Instead, this tale is intended to provide an example of a single network operator’s journey to autonomous networking. The goal here is to help give others a glimpse of the potential benefits and opportunities to be gained by moving toward a more automated service delivery engine.
In the future, our hero’s journey will indeed lead to a fully autonomous network infrastructure. While he once dreamed of a network that would deliver new services quickly and efficiently, he has gained the ability to explore and seek out new market opportunities, designed to grow his business, now and for the foreseeable future. And his network will increasingly rely on machine learning, or artificial intelligence, to maintain performance levels that support primary customer needs, autonomously.