Network Digital Twins: Next Generation Active Control for Telecoms

This Industry Viewpoint was authored by Dr. William Bain, CEO, ScaleOut Software

Telecom networks have steadily evolved toward more complex and dynamic architectures, fueled by 5G expansion, edge computing deployments, and the rapid growth of connected devices. However, as capabilities increase, so do operational pressures. Network teams must maintain consistent quality, respond quickly to disruptions, and implement changes without impacting customers. Traditional monitoring and management tools designed for landlines and small, centralized networks were not built to manage this level of scale. As a result, operators are seeking new approaches that provide the speed and adaptability required in today’s live telecom environments. To meet these challenges, telecom innovators are turning to a new class of technologies that make networks more intelligent, predictive, and responsive.

Creating a Dynamic Model of an Operational Network

Imagine having a live, interactive model of an entire telecom network that updates continuously, predicts where problems will occur, and enables changes before applying them in the real world. This is the promise behind an emerging technology designed to meet today’s operational challenges: the Network Digital Twin (NDT). At its core, the concept of an NDT represents a fundamental shift from reactive troubleshooting to proactive network intelligence.

An NDT is a software-based, dynamic simulation of a telecom network that continuously synchronizes with real-world data. Unlike prior simulation tools, it uses live telemetry, predictive modeling, and AI to mirror real-time network conditions and dynamically adjust operational parameters. This technology can detect anomalies and bottlenecks, then evaluate in real time potential network optimizations such as traffic rerouting or resource reallocation. It enables operators to plan, test, and respond without disrupting live infrastructure. By doing so, an NDT gives operators an operational model of their live network, one that dynamically tracks and models the environment it represents.

An NDT enables operators to spot issues before they escalate, test remedies in a virtual environment with minimum risk, and deploy changes with greater confidence. It provides a new set of tools for optimizing performance and improving the efficiency of day-to-day operations.

Scaling Network Digital Twins for Real-Time Impact

Delivering the capabilities of an NDT in a live telecom environment is no small feat. To be effective, an NDT must track and model millions of network elements spanning a wide geographical area, refresh its model in near real time, and incorporate the complex interactions and dependencies between hardware and software network components. It must also process incoming telemetry fast enough to support decisions in the moment, not after the fact. The success of an NDT depends on its ability to evaluate network performance and model potential adjustments at the same pace as the network itself.

A software technology call in-memory computing (IMC) can provide the speed and scalability needed to meet these demands. It enables NDTs to scale to track and model millions of interconnected components and process updates as network conditions change. This technology maintains state information about network components in a scalable, in-memory store, and it analyzes telemetry for each component in milliseconds. It helps operators continuously evaluate how changes ripple across the network, identify emerging issues, and resolve them before customers notice a problem. By combining speed, scale, and computing power, IMC helps an NDT provide the real-time operational resources needed to drive better, faster decision-making. Real-time intelligence like this becomes especially valuable in environments where network conditions shift rapidly and customer expectations leave no room for delays.

Real-Time Insights in Action

Consider a major sports event drawing thousands of attendees to a stadium. As fans arrive and the game begins, mobile traffic in the surrounding area spikes with heavy demand for video uploads, live streaming, and messaging. Likewise, network traffic supporting video streaming at homes in the surrounding area also spike. Network operators must reallocate resources to accommodate these dynamic conditions while at the same time maintaining service commitments to all customers and minimizing energy costs.

To manage these complex demands, an NDT monitors network performance in real time, and it can take advantage of in-memory computing to track a vast number of network elements as conditions change. The NDT also can incorporate machine learning to evaluate signaling patterns and look for emerging issues with network equipment. It continuously aggregates data, computes delays, and pinpoints where network congestion is likely to arise. The NDT also takes advantage of generative AI to model network changes. Gen AI can recommend the best way to reallocate resources and optimize quality of service so that operators can proactively address network congestion.

The NDT tests proposed strategies for resource reallocation within seconds, allowing operators to make targeted changes to the live network with minimal risk of disruption. The result is a smoother experience for fans and more efficient use of resources during a high-demand, time-sensitive event. Scenarios like this highlight how the NDT can transform network management from reactive problem-solving to proactive optimization.

A Smarter Path Forward

As telecom networks continue to expand in scale and complexity, the need to manage them with speed and agility has become critical. The NDT combined with in-memory computing offers a compelling software technology for tracking and modeling these highly complex systems, enabling operators to quickly identify issues and safely implement changes to live networks. NDTS empower telecoms networks to maintain service quality, optimize performance, and ensure network resilience even as the size of telecom networks sets new records.

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About the Author
Dr. William L. Bain is founder and CEO of ScaleOut Software, which has been developing software products since 2003 designed to enhance operational intelligence within live systems using scalable, in-memory computing technology. Bill earned a Ph.D. in electrical engineering from Rice University. Over a 47-year career focused on parallel computing, he has contributed to advancements at Bell Labs Research, Intel, and Microsoft, and holds several patents in computer architecture and distributed computing. Bill founded and ran three companies prior to ScaleOut Software. The most recent, Valence Research, developed web load-balancing software and was acquired by Microsoft Corporation to enhance the Windows Server operating system.

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Categories: Artificial Intelligence · Industry Viewpoint