As networks grow larger and more complex, the labor and costs associated with manually managing them have outgrown IT capabilities. IT leaders have in turn sought out opportunities to reduce manual oversight and management, instead relying on network automation. Network automation is the provisioning, configuring, deploying, and managing of network devices using software with self-executing processes. It reduces the repetitive processes required of IT departments to scale, optimize, and protect both physical and virtual network infrastructure at a corporate level. For example, an automated network requires no significant human labor to set up the network infrastructure, often called “zero-touch” provisioning.
Network automation works via a layer of software implemented across networking devices like routers, switches, and more. The network automation software leverages a set of application programming interfaces (APIs) to establish communication with hardware and network devices. Through the APIs, the network can then be configured as needed.
Artificial intelligence (AI) and machine learning (ML) techniques can take automation even further, essentially allowing an AI-enhanced automation engine to evaluate network and application performance, then respond by making adjustments to the network configurations automatically.
Using AI and ML techniques, network automation tools evaluate historic patterns of bandwidth consumption and performance, then configure all physical and virtual network assets using a set of known-good parameters.
Think of it this way: If a corporate network is a complex puzzle with 10,000 interlocking pieces, then networking automation is like a robot that can understand in an instant how every puzzle piece fits. It can then place the next piece accordingly—and do so at a speed and scale beyond what is humanly possible. Because the solution learns using past experience data, it will get better and faster at solving every future puzzle.
Network automation is important because it solves business problems:
Productivity: Managing the network and application performance is difficult and time-consuming for IT teams. Networks can be rigid, complex, and fragmented systems. They are typically operated manually with a small team of people focused on identifying the root cause of service degradations while handling all fault management, configuration management, bandwidth allocation, and security. But this work is time consuming with the average network manager spending 20 hours a week troubleshooting the network.
Human error: Because the human brain is limited in its capacity to evaluate complexity using mountains of data, it’s often difficult for people to understand what’s happening inside the network. It can be even harder in multi-cloud environments where there are too many vendors, systems, dashboards, and policies to evaluate. This is why network automation powered by AI is critically important today. Network automation reduces the complexity of a corporate network which affects the amount of labor required.
Network Costs: When leveraged alongside virtualized network hardware, the cloud, and converged devices (e.g. a single piece of hardware that serves as a router, firewall, and SD-WAN endpoint), network automation reduces a company’s overall costs. Whether the network uses equipment that is physical, virtual, or a hybrid of both, the combination of AI, automation, and software at every network endpoint provides real-time analytics on the condition of the network. By performing constant “health checks” on the network at a global scale, network automation empowers IT managers with the business agility needed to run the network more efficiently.
The base-level requirements for an automated network are :
A software-powered network control plane and management system: Commonly used in software-defined wide area networks (SD-WAN), this software “abstracts” the network signaling, configuration, and administration functions of networking equipment like routers or switches. A software-defined and centralized control plane also enables the end-to-end network visibility required for network automation
Built-in artificial intelligence and machine learning capabilities: An automated network uses AI/ML to analyze and configure network devices automatically to provide users the best experience
Detailed network analytics: A crucial component to all automated networks, real-time network analytics are required for AI/ML components to not only set up but also optimize the WAN/LAN
On a typical corporate WAN or LAN today, setting up routing tables for IP traffic is often a time-consuming and labor-intensive process managed by an experienced IT manager or network architect. However, an automated network can use real-time analytics data fed into the machine learning-engine to determine optimal network packet routes on a LAN or WAN faster than humanly possible. Artificial intelligence can then push out that optimal configuration to all network routers on a global scale via the network control plane software in minutes.
As networks become larger and more dispersed, complexity will only continue to grow. Although we still have a long way to go to achieve fully automated networking, the proliferation of AI in tandem with software-defined networking promises to further reduce the burden on IT teams and continually transform network management.
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