The first of these is a management take on something that’s already becoming visible in a broader way: absorbing the network into something else. Companies have said for years that the data center network, the LAN, is really driven by data center hardware/software planning and not by network planning. They’re now finding that a broader use of hybrid cloud, where the cloud becomes the front-end technology for application access, is pulling the WAN inside the cloud. The network, then, is becoming less visible, and thus explicit network management is becoming less important.
Ironically, this magnifies the potential role for AI. With everything getting subducted under the high-level view of sustaining application quality of experience (QoE), there are too many moving parts in an experience to allow for meaningful problem identification, isolation, and resolution. Enterprises would love to have a true “QoE console,” and they believe AI is the tool most likely to make that wish come true. Progress is visible here, enterprises say, but some vendor is going to have to take the lead to transform their options. They don’t believe a network vendor is likely to be the first mover.
Digital twins plus AI could broaden network management scope
The second development gaining attention is being proposed by a number of vendors, the largest being Nokia. It envisions using “digital twin” technology, something most commonly associated with IoT and industrial metaverse applications, to construct a software model of the network based on digital twins of network devices. With this approach, the network becomes in effect an industrial system, and potentially could then be monitored and controlled by tools designed for industrial IoT and industrial metaverse deployments.
One problem is a lack of support from vendors that enterprises know. Vendors that support digital twin modeling, like Forward Networks, have little name recognition among enterprise network planners. Only 29 of the 169 enterprises looking for new visions in network operations knew about Nokia’s ideas, partly because they had no engagement with Nokia to speak of, and partly because the new approach is part of Nokia’s Technology 2030 initiative, which was only announced at the end of October. If the digital-twin concept develops, it could reinforce AI too.
AI is typically viewed as a means of better exploiting information about network behavior, using it to discover issues, isolate problems, and recommend solutions. One challenge to that approach is the question of context or state. What is normal operation? How are the elements of a network supposed to relate to one another? What happens if we apply this suggested remedy? The answers to those questions might be discoverable in machine learning, but how long would it take for AI to learn enough about network operations to dig them out?
Digital twinning of a network could create a model that describes the relationship between elements and not just the state of the elements. Because that model represents an operating state, you could define multiple models to represent various acceptable states and expected error states. You could even run simulations through the model to see what outcomes would be likely before you took a step to change device or network behavior. Add AI to this, and you’d have an AI management tool that could not only see what’s going on, but also see what should be, and what might happen. It’s hard not to see that as a powerful path toward addressing both those network management scope issues.
