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What is Network Agility?

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Network Monitoring
(Image: Pixabay)

As I was reviewing the results of the Sirkin Research/LiveAction survey titled: Top Trends Transforming Network Operations, one specific question caught my eye. When asked to choose a top business goal for 2019, over one-third (34 percent) of IT decision-maker respondents answered that it was to "improve network agility." This particular answer was the number one choice.

Thus, it must be important to many organizations. Yet it left me wondering, what does "network agility" truly mean? Agile software development is an easy enough concept to grasp. But as it relates to the network, I honestly wasn't sure what makes one necessarily agile. After all, it sounds rather vague and potentially could mean different things to different people. After a bit of research, however, I discovered that network agility is rather well defined and consists of four key components. Let's go over how to properly define network agility and its core components while also providing real-world examples of how one can implement network agility into any production network.

Network agility can be defined as the speed at which a network can adapt to change while maintaining resiliency, security, and management simplicity. Much of how network agility is accomplished today includes the use of modern architectures and technologies such as software-defined/intent-based networking, artificial intelligence (AI), analytics, and advanced automation. Within this construct, network agility is comprised of the following categories:

Network automation: One way to increase network agility is to leverage automation for the handling of processes that were previously performed manually. Automation can be used to assist with overall network performance and efficiency. Improvements can be made using intelligent data flow mechanisms. These mechanisms use network telemetry data, health probes, and AI to analyze application data flows and the various paths they can be sent over. Automation can then leverage the analytics processed through the AI to choose the most efficient path based on the criticality of each individual data flow.

Deployment speed and scalability: From a deployment standpoint, both speed to deployment and scalability are key areas that are addressed through network agility.  The use of zero-touch provisioning and centralized control-plane architectures are two examples where speed of provisioning new network segments and services can be enhanced. Then looking at scalability, automation can once again be put to use alongside virtualization. Automation can be used to create and deploy pre-defined network templates that can be deployed with just a few clicks. The result is the deployment of a network using virtual network appliances and network functions that are deployed with uniform network policies throughout the private LAN and into the public cloud.

Network visibility: The best way to maintain long-term network agility is by having the proper level of visibility into a network from a data flow perspective. Deep data insights provide a granular view of the end-to-end operational health of a network. This level of visibility allows network architects to understand better what will happen when changes to network flows are disrupted, change, or are added to. Legacy network monitoring tools such as SNMP, traceroute, and ping are no longer enough if your goal is to build an agile network. Instead, modern network analytics platforms that source streaming network telemetry data and analyze it using AI is a far better choice.

Streamlined information security: Lastly, no network can be considered “agile” unless the underlying security processes are both robust and streamlined. Adding the multiple – yet critically important – layers of network security into a network has become a huge management burden in many organizations. Network agility processes and tools help to eliminate this time sink through centralized control, software-defined segmentation, and access control/management. It can also be accomplished by using intelligent identification intelligence and automated security policy enforcement of identified end devices.