Key takeaways:
- Network digital twins enable a higher level of decision-making than allowed by traditional network modeling methods.
- They can improve networks used in data centers, fiber optics, telecommunications, defense, and many more industries.
- Network digital twins, combined with artificial intelligence, can model complex phenomena seen in networks.
The communication network infrastructure of data centers, telecom companies, and militaries are vast and complex. Their operations, uptime, performance, and security have always required constant monitoring. The increasing popularity of cloud computing and artificial intelligence has further complicated these networking concerns. Additionally, regulators, investors, and customers are paying increasing attention to the sustainability, energy consumption, and emissions of networking infrastructure.
In this demanding business environment, digital twin technology and automation offer solutions for many networking challenges.
But what exactly is a network digital twin? How does it address these challenges? Find out in this blog.
What is a network digital twin?
A network digital twin is a highly detailed virtual model of a real wired or wireless network that models its architecture and components while accurately mirroring its real-time state, configuration, and behavior.
This digital model is a safe environment for analyzing, optimizing, and predicting network performance and behaviors under various scenarios.
How is a network digital twin different from traditional network modeling?

A network digital twin is far more powerful than traditional static network models or simulators as outlined below:
- Dynamic modeling: A network digital twin models the dynamic behavior and functionality of a wired or wireless network.
- Real-time data from the physical network: It communicates with the physical network and its network management systems to obtain real-time data like its network metrics and traffic.
- Reconfiguration commands to the physical network: It can send reconfiguration commands to the physical network’s devices for optimizing some aspects of the network.
- Higher-order network modeling: It can use physical and environmental data collected from temperature sensors or data acquisition systems to inform its optimization strategies. This is unlike traditional network management where network planning generally does not consider such aspects directly.
- End-to-end lifecycle modeling: Another major difference is that a network digital twin replicates the physical network for its entire lifespan. So the twin must accommodate long-term activities like equipment upgrades, downtimes, and decommissioning. In contrast, network models and simulators are generally only used in the initial design stages until the network is deployed.
These capabilities show that in many ways, network digital twins are more powerful than even their physical siblings. Network twins can transcend the restrictions of physical networks, such as costs, security policies, and allowed configuration changes. Instead, the twin is an enabler of a higher level of decision-making that’s based on aggregate network performance metrics, environmental factors, regional traffic patterns, sustainability goals, and other such factors that are traditionally outside the scope of network management.
What key elements and components are included in a network digital twin?
The most important component of a network digital twin is the network topology. It models:
- all the relevant network devices with their layer two and layer three identifiers
- their connections relevant to the use case
Other key components of network digital twins include:
- device characteristics of the network equipment
- control and application traffic as well as traffic profiles for various use cases
- network and device configurations
- various network protocols and their required data for realistic emulation
- cybersecurity aspects like firewall rules, device vulnerabilities, and possible cyber attacks
- physical environment of the network
- device locations and surrounding land topology for use cases like telecom networks
How is security addressed in the development and use of network digital twins?
The physical network’s cybersecurity posture, consisting of its network security and data security aspects, is a crucial input for developing a high-fidelity network digital twin.
Security-related inputs to network digital twins include:
- Vulnerability assessments: Vulnerabilities in the physical network are identified using vulnerability scanners. These assessments become inputs to the modeling of cyber attack scenarios using the network digital twin.
- Network security device configurations: The configurations of security devices like firewalls, intrusion detection and prevention systems, and physical access control systems are crucial inputs to digital twin scenarios.
These inputs are used for various security-related initiatives outlined below:
- Model cyber attack scenarios: Different types of cyber threats are modeled and simulated in the controlled, isolated virtual environment of a network digital twin. They include denial of service, malware, intrusion, and eavesdropping attacks. These simulations enable security engineers to understand network behaviors and operational functionalities when under attack.
- Simulate attacks using artificial intelligence (AI): AI techniques like reinforcement learning can be trained to test long and complex exploit chains on the network digital twin. Generative neural networks like large language models can extract useful information from security-related vulnerability, incident, and assessment reports and dynamically generate appropriate exploit code. This code is run on the digital twin to simulate a cyber attack and study its impacts.
- Discover deeper vulnerabilities: Realistic simulations often uncover previously unknown vulnerabilities in the network. These new weaknesses are added to the vulnerability database for future simulation cycles.
- Test mitigation and remediation measures: Cyber attack mitigation and remediation workflows can be tested and hardened on the network twin in preparation for real attacks.
- Generate actionable remedies: The above activities on the digital twin help to discover more resilient network architectures and device configurations, which are then applied to the physical network.
How does artificial intelligence enhance network digital twin capabilities?

In large enterprise networks, many intangible interactions and invisible cause-effect relationships exist. For example, a changed router configuration in part of a network may result in a kind of butterfly effect on the latency of an entire segment of the network.
AI enables the modeling and discovery of such emergent phenomena and latent relationships that lie hidden in the network and surface only under specific conditions. The training data for such AI models is obtained by simulating relevant traffic on the digital twin and recording network configurations and metrics.
Some concrete examples of such AI-enabled digital twin studies include:
- Cybersecurity scenarios: As explained earlier, AI techniques like reinforcement learning run on digital twins to simulate what-if scenarios.
- Higher-order network optimization: Machine learning (ML) techniques applied to network data from digital twins enable advanced optimizations that are generally not considered when managing typical physical networks. ML models can use aggregate metrics like a geographical area’s net throughput or environmental factors like power consumption — factors that are normally outside the scope of network management — to suggest optimizations.
- Failure analysis: Patterns in network failures and outages can be identified using algorithms like anomaly detection and time series forecasting on the metrics collected from the digital twin. Troubleshooting procedures for them can then be formulated and verified on the twin.
What are the main challenges in implementing and maintaining a network digital twin?
Implementing and maintaining network digital twins are not without challenges. Some major ones are outlined below.
1. Modeling the complexity of real networks
Networks can involve hundreds to thousands of network devices, each with its own complex configuration and behavior. For example, 5G/6G telecom and data center networks are vast and complex.
Aim to accurately model the network dynamics resulting from the interplay of relevant communication protocols, system configurations, network topology, physical network environment, and application traffic observed in the specific scenarios.
2. Accurate modeling and simulation
The modeling and simulation must be at the speed and scale required to get statistically valid and meaningful results without compromising accuracy.
3. Differences between real and virtual networks
Over time, there is a risk of the configurations and behaviors programmed in the network twin diverging away from those of the underlying physical network. To minimize this, use automation to regularly sync the networks as well as sync them after any manual configuration changes.
4. Real-time integration between the two networks
Syncing requires real-time integration between the physical network’s hardware and software and the ones modeled in the twin. This can be very challenging because of multiple vendors and a lack of standardization in hardware and software interfaces. Minimize these issues by limiting the scope of the network twin to address specific problems.
5. Preventing the network digital twin from turning into a vulnerability
The network twin contains deep knowledge about its physical network. The twin also has hardware and software connections to the physical sibling and its network management systems.
All these are potential security vulnerabilities that can give malicious parties unprecedented access to the physical network’s data and resources. So, even as the digital twin is being tightly coupled with the physical network, it must simultaneously ensure its own data security and the security of all its connection points.
What are some practical use cases for network digital twins in various industries?
Let’s look at some practical uses of network digital twins in different industries.
Data centers
Network digital twins enable dynamic network planning and optimization based on device locations, power usage, power capacity, and other such factors.
For example, temperatures and power consumption can be measured using internet of things (IoT) sensors or data acquisition (DAQ) systems connected to racks. If these metrics start crossing some thresholds, traffic can be throttled or dynamically routed through other paths to keep them under control.
Such measures are increasingly necessary to comply with sustainability and emission targets mandated by regulations.
Optical networks
Network digital twins can dynamically optimize routing and power configurations based on measurements of physics phenomena like attenuation, dispersion, and nonlinear effects in optical networks.
5G/6G communication networks
In mobile networks, network twins can use real-world factors like channel congestion and radio frequency power to dynamically optimize beamforming parameters, network element configurations, power usage, and more.
If such dynamic changes are applied directly to the physical network, it can degrade the quality of service for users or inadvertently breach emission power limits. However, engineers can safely experiment with them on the digital twin to find optimal configurations.
A concrete example of this is the use of AI and network digital twins for 5G mobile edge computing networks to optimize resource allocation and energy savings using reinforcement learning.
Military and defense
Fig 1. Battlefield network layout during an amphibious landing scenario
In the military and defense domain, network digital twins are used for wargaming and network resilience testing.
Network twins can be created for constructive networks, which are other virtual networks created in battlefield simulators and other computer-generated forces (CGF) tools.
Fig 2. Wargaming with network digital twin
For example, a constructive network may model wargaming scenarios that include the deployment of infantry units, mechanized equipment, airborne early warning systems, and military satellites. The network twin models the wired and wireless networks that interconnect them. Then co-simulations are run to try out various scenarios. The digital twin and the constructive network simulate different aspects simultaneously while being subjected to offensive measures like signal jamming or eavesdropping.