Experts set the record straight on new networking technologies

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New networking technologies are gaining traction in the industry as practitioners seek innovative ways to manage their complex environments.

However, misconceptions swirl around what new networking technologies can do for an organization and what they mean for networking teams. For example, organizations view the use of a digital twin for network management purposes as a worthwhile investment. In the same breath, many networking professionals believe that AI is the catalyst that will trigger a chain reaction of layoffs in the IT industry.

Three networking bloggers set the record straight on what these networking technologies can do for network management, and they identified approaches that can simplify the implementation process.

Identifying the Risk vs. Reward for Digital Twins in Network Management

It is possible for a network professional to create a digital twin model of a network the same way an engineer would create a model of an IoT system. But, before doing so, practitioners should weigh the risks and benefits of implementing a digital twin model for network management, CIMI Corp. president Tom Nolle wrote on his blog.

Digital twins would be more practical in software-defined networks that depend on explicit routing through a centralized controller, Nolle wrote. A digital twin could create an abstraction layer that represents different devices and elements in the environment. Digital twins would work best in networks that use static routing because they don’t constantly change, like with adaptive routing.

Most networks use adaptive routers and switches, which means they adjust and interact with each other based on network behavior. These networks would not benefit from a digital twin model because the model could interfere with how routers readjust to the network. Disabling adaptive behavior in devices could eliminate this risk, but Nolle wrote that there would be little to be gained from configuring a model in these types of networks.

“Lack of risk is not an advantage,” Nolle wrote. “Can we identify anything interesting that we could do with the digital twin model? Yes, but not much.”

Nolle identified a few ways a digital twin model with abstraction could improve network management, such as the following:

  • support for software-defined networking (SDN) and adaptive routing;
  • operate a network management system; Where
  • consolidate mixed routers and virtual networks.

Despite these use cases, many vendors are reluctant to offer multi-vendor abstraction, largely because SDN has not advanced enough to offer these services. Similarly, teams are equally reluctant to deploy new technologies into their systems. Nolle wrote that digital twin models could be difficult to put into practice, but network professionals should “wait and see” how they might work in network management.

Network automation is less common than you think

All the buzz surrounding network automation might be nothing more than a few loud whispers. Recent research from Gartner has shown that adoption of network automation is less widespread than the market implies. More than 50 network automation tools are available to businesses, but automation affects less than 35% of network activity, Andrew Lerner, research vice president at Gartner, wrote in a blog post.

Currently, few organizations automate more than half of their network activities. A clear divide exists between organizations with automated networks and those without. Companies with automated networks are more vocal in the industry, thereby creating a “false sense of widespread network automation,” Lerner wrote. This results in vendors offering options to a small slice of the market rather than the majority.

In its “Market Guide for Network Automation Tools” report, Gartner outlined some barriers preventing the adoption of network automation tools, including budget constraints, limited skills, and a lack of confidence in using the tools. Gartner recommended that organizations schedule easier “quick win” activities to start the automation process.

Some of these quick wins include the following:

  • create trouble tickets with network information;
  • automation of device configuration archives; and
  • enable or disable monitoring tools when implementing a change.

AI helps rather than hinders

One of the biggest concerns about AI — and one of the main reasons AI adoption is limited — is the idea that implementing the technology will lead to mass layoffs in the workforce. networking industry, as engineers will lose their jobs to machines. However, the process of an engineer programming AI to automate network tasks suggests that AI will favor teams, Foskett Services network analyst Tom Hollingsworth wrote on his website.

AI would automate trivial and mundane tasks of network operations, which are usually repeated tasks. While the burden of these tasks falls on the AI, network professionals would be free to focus on new or critical tasks. This innovation, Hollingsworth noted, would allow AI to promote professionals into higher roles because engineers can focus on complex tasks that AI cannot perform.

“AI doesn’t kill jobs. It kills tasks,” Hollingsworth wrote. “If your job is a collection of tasks that need to be done, then it’s worth asking why it’s so easy to replace it with an AI system.”

Although AI is replacing some responsibilities, that does not mean that network professionals will no longer be needed in these areas. A human will be the one training and configuring the AI ​​algorithm, as well as updating hardware or procedural changes.

“Given these constraints, AI will work for you, not against you,” Hollingsworth wrote.

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