New network technologies are gaining traction in the industry as practitioners look for innovative ways to manage their complex environments.
However, misconceptions circulate about what new network technologies can do for an organization and what they mean for network teams. For example, companies consider using a digital twin for network management purposes as a worthwhile investment. In the same breath, many network professionals believe that AI is the catalyst that will set off a chain reaction of layoffs in the IT industry.
Three networking bloggers clarify what these network technologies can do for network management and identify approaches that can simplify the implementation process.
Identify risk and reward for digital twins in network management
It is possible for a network professional to create a digital twin model of a network just as an engineer would create a model of an IoT system. But before that, practitioners must weigh the risks and benefits of implementing a digital twin model for network management, Tom Nolle, president of CIMI Corp., wrote on his blog.
Digital twins would be more practical in software-defined networks that rely on explicit routing via a centralized controller, writes Nolle. A digital twin could create an abstraction layer that represents various devices and elements in the environment. Digital twins would work better on networks that use static routing because they aren’t constantly changing like they are with adaptive routing.
Most networks use adaptive routers and switches, meaning they adapt 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 routers’ re-adaptation to the network. Disabling adaptive behavior in devices could eliminate that risk, but Nolle said there would be little gain in configuring a model in these types of networks.
“No risk is no benefit,” Nolle wrote. “Can we identify anything interesting that we could do with the digital twin model? Yes, but not much.”
Nolle identified some ways a digital twin model with abstraction could improve network management, such as: B. the following:
- supports software-defined networking (SDN) and adaptive routing;
- operating a network management system; or
- Consolidate mixed routers and virtual networks.
Despite these use cases, many providers are reluctant to offer a multivendor abstraction, mainly because SDN is not mature enough to offer these services. Likewise, teams are reluctant to use new technologies in their systems. Nolle wrote that digital twin models could be difficult to implement in practice, but network professionals should “wait and see” how they might work in network management.
Network automation is less common than thought
All the talk of network automation might be little more than a few loud murmurs. Recent Gartner research has shown that network automation adoption is less widespread than the market suggests. Over 50 network automation tools are available for businesses, but automation accounts for less than 35% of network activity, wrote Andrew Lerner, vice president of research at Gartner, in a blog.
Few organizations currently automate more than half of their network activities. There is a clear division between organizations with automated networks and those without. Companies with automated networks are more vocal in the industry and consequently create a “false sense of widespread network automation,” Lerner wrote. This results in vendors offering options for a small portion 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 capabilities, and a lack of confidence in using the tools. Gartner recommended that organizations code simpler “quick-win” activities to begin the automation process.
Some of these quick wins include the following:
- creating trouble tickets with network information;
- Automation of device configuration archives; and
- Enable or disable monitoring tools when implementing a change.
AI helps instead of hindering
One of the biggest concerns about AI — and one of the biggest reasons for the limited adoption of AI — is the notion that the technology’s implementation will lead to mass layoffs in the networking industry as engineers lose their jobs to machines. However, the process of an engineer programming AI to automate network tasks suggests that AI will empower teams, Tom Hollingsworth, a network analyst at Foskett Services, wrote on his website.
AI would automate the trivial and mundane tasks of network operations that are typically repetitive tasks. Because the burden of these tasks is shifted to AI, network experts can focus on new or critical tasks. This innovation, Hollingsworth said, would allow AI to promote professionals to higher roles because engineers can focus on complex tasks that the AI cannot perform.
“AI doesn’t take away jobs. She’s taking tasks away,” Hollingsworth wrote. “If your job is a collection of tasks that need to get done, then it’s worth asking why it’s so easy to replace it with an AI system.”
While AI will replace some responsibilities, that doesn’t mean that network experts in these areas are no longer needed. A human will train and configure the AI algorithm, as well as update hardware or procedural changes.
“Given these limitations, AI will work for you, not against you,” Hollingsworth wrote.
