Intent-based networking is providing easier operations for today’s complex, software-defined networks. But, as these networks grow increasingly larger, the vast programmability of devices and flexibility in their configuration leads to unimaginable levels of complexity. A network analytics engine, driven by Artificial Intelligence and Machine Learning (AI/ML), is simply the only way for humans to navigate this complexity. This short primer will explain why we need AI/ML, the basic concepts of AI/ML related to network analytics, and the role they play in intent-based networking.
First let’s look at the challenges facing IT teams today.
- There is a proliferation of client devices connecting to the network such as laptops, smartphones, cameras, sensors, machines, robots, thermostats, lighting, etc. Each one of these client devices requires a distinct set of parameters to be configured in the network in order to guarantee user experience and security.
- Users are moving to wireless as the primary medium of network connectivity in the campus. Wireless communications are much more complex in the number of parameters that need to be configured in order to assure an optimal user experience. Additionally, the wireless medium is very dynamic, and performance can vary depending on the number of users, services, and applications and levels of interference.
- Applications are moving to the cloud. The network architecture needs constant updating to support the many different entrance points for data into the campus and the diverse paths that the data will travel.
The goal of intent-based networking is to automate the network to dynamically meet business priorities and intent; the network is constantly learning and adapting to securely maintain business intent. First, business intent is translated into policy based on user or device identity and application requirements for that job role or device function. For example, a salesperson’s job requires reliable and quality video conferencing, finance may require low-latency connectivity to an off-shore banking database, and a factory emergency shut-off valve would need instant transmission of very low amounts of data. Then, these policies are translated into network infrastructure configurations and deployed via controller-based automation throughout the network. Finally, the network constantly monitors, collects data, and analyzes to make sure business intent is being realized.
Supporting these new challenges means collecting information in the form of real-time telemetry throughout the network, in order to track Key Performance Indicators (KPIs) and identify anomalies – things underperforming in the network. But the number of anomalies and alerts being generated is simply too much to be useful. Today’s networks are generating massive amounts of data, and this means too much noise for humans to deal with in a timely manner. AI/ML can learn to differentiate between important alerts and trivial anomalies, thereby reducing this noise.