How digital twins and AI will impact the network

Network evolution is taking place on multiple fronts. Modernisation efforts include the cloud, hyperscale change and the application of digital twins and AI in network architecture and planning.

Today the world has more than 900 Hyperscale datacenters. And there are more than 8,600 datacenters geared towards colocation and wholesale leasing. There are therefore proven trends in the repatriation of workloads from cloud computing to the datacenter.

ABB research indicates that only 29% of datacenter decision-makers say that their current facilities meet their needs, and only 6% say that their data centres are upgraded ahead of their needs. Security (45 per cent) and bandwidth (43 per cent) are the most needed upgrades. But Digital Twins and AI are starting to rise up the priority lists. Especially with regard to the veins that support them: their connectivity solutions.

Let’s see.

The proliferation of connected devices is unlikely to slow down any time soon. And 5G technologies are bringing more intelligence to the edge of the network. There’s a connected revolution happening, from smart personal devices to the inside of the walls and ceilings of modern buildings. For example, smart buildings, where devices converge on a shared IT infrastructure, can provide more operational functions and improve the occupant experience.

Exponential data growth is also forcing datacenters to be closer to their customers, resulting in more Edge Computing deployments and forcing datacenter operators to increase speed, security and efficiency while minimising latency. Hyperscale datacenters – and the cutting-edge technologies that accompany them – are turning these unprecedented challenges into opportunities.

Therefore, the implementation of 5G is putting unprecedented pressure on data centre infrastructure around the world, with the introduction of new services, IoT verticals and the intelligent edge that powers them. The increased complexity of the end-to-end network challenges old deployment schedules, while raising the bar for performance, efficiency and reliability.

To keep up with this rapid change, it’s no surprise that datacenters are undergoing a major transformation – from design to scale and the way they are powered, organised and run. Working with digital twins and AI is helping infrastructure leaders revolutionise their network design and approach to data centre connectivity.

For example: the use of AI, ML and digital twins makes it possible to deploy often complex 5G networks with zero risk, explain the VIAVI analysts.

• Digital twins make it possible to predict the effect of updates and any configuration changes on end-user services.

• In AI and ML, digital twins allow you to calibrate your ecosystem with precise network data to make it as close as possible to the physical network.

• Finally, these solutions make it possible to take advantage of what-if scenarios using large, real historical data and ML-modelled RAN propagation scenarios that mimic the real environment.

You can create a scalable platform for application development and network connectivity validation by leveraging a digital twin at the network layer. Specifically, these tools make it possible to find real opportunities to facilitate efficiency and growth.

• Digital twins enable innovative applications for new vertical sectors in realistic scalability, resilience and dynamic scenarios. This level of innovation is impossible without a real digital representation of a network.

• Suppose your business unit wants to test an entirely new product or market segment that depends on your network. In this case, your digital twin can validate, score and fine-tune new application performance requirements through closed-loop iterations and real data modelling for RAN and Let’s see how these emerging testing solutions are key networks.

• Digital twins enable management, network assurance and troubleshooting without interrupting network operation.

Moral of the story: before any real-world deployment, digital twins and AI make it possible to test and certify configurations, optimisations and intent-based policies before using them on real networks.

Conclusion:

• Infrastructure leaders must adopt a more holistic approach to managing and testing their networks to support these connections.

• Datacenter monitoring and orchestration tools are becoming increasingly agile, automated and virtual to support new datacenter infrastructure needs.

• Rigorous testing of virtual, physical and cloud-based infrastructures will play an integral role in the mission to achieve uptime 24 hours a day, 7 days a week, 365 days a year.