Creates a mathematical model (digital twin) of enterprise networks to verify intent, security, and configuration.
China · Company
Promotes the Autonomous Driving Network (ADN) concept which relies heavily on network digital twins for self-optimization.
Offers the Quantum Engineering Toolkit (QET) and Labber software for instrument control and pulse generation.
Provides automated testing and assurance solutions specifically for validating network slice performance and isolation.
Through its acquisition of Apstra, offers intent-based networking software that maintains a real-time digital twin of data center fabrics.
Provides a 'Dynamic Map' platform that acts as a live digital twin for network automation and troubleshooting.
Citymesh
Belgium · Company
A B2B operator specializing in private 5G networks, utilizing digital twins to plan coverage for industrial clients.
Network infrastructure has grown exponentially complex, with modern telecommunications systems comprising millions of interconnected components spanning fiber-optic cables, cell towers, routers, switches, and data centers. Traditional network management approaches struggle to maintain visibility across this sprawling architecture, often leaving operators reactive rather than proactive when addressing performance issues or capacity constraints. The challenge intensifies as networks evolve toward 5G and beyond, where ultra-low latency requirements and massive device connectivity demand unprecedented precision in network operations. Network Digital Twin technology addresses these challenges by creating a comprehensive virtual replica of the entire physical network infrastructure that mirrors real-world conditions in real-time. This digital counterpart ingests continuous streams of data from sensors, performance monitors, and network elements to maintain an accurate, up-to-the-minute representation of network state, traffic patterns, and component health. The system employs advanced modeling techniques to capture not just the static topology of network assets but also the dynamic behaviors and interdependencies that characterize actual network operations.
The transformative power of this technology lies in its ability to serve as a risk-free testing environment where network operators can simulate proposed changes before implementing them in production systems. Rather than deploying new configurations or capacity upgrades with uncertainty about their impact, engineers can model these modifications within the digital twin and observe predicted outcomes across multiple scenarios. Machine learning algorithms analyze historical performance data within the twin to identify patterns that precede equipment failures, enabling predictive maintenance strategies that prevent outages rather than merely responding to them. This capability proves particularly valuable for optimizing traffic routing, where AI-driven analysis can evaluate thousands of potential path configurations to identify solutions that maximize throughput while minimizing latency and congestion. The technology also enables automated remediation workflows, where the digital twin continuously evaluates network health and triggers corrective actions when anomalies are detected, significantly reducing the mean time to resolution for network incidents.
Early deployments of network digital twins have emerged primarily among major telecommunications carriers and cloud service providers seeking to manage increasingly complex infrastructure at scale. These implementations demonstrate measurable improvements in network reliability, with some operators reporting significant reductions in unplanned downtime through predictive maintenance capabilities. The technology shows particular promise for managing the transition to software-defined networking architectures, where the boundary between physical and virtual infrastructure becomes increasingly fluid. As networks continue to densify with edge computing nodes and IoT connectivity expands, the digital twin approach offers a scalable framework for maintaining operational visibility and control. Industry analysts note that this technology aligns with broader trends toward autonomous network operations, where AI-driven systems progressively assume responsibility for routine optimization and troubleshooting tasks, freeing human operators to focus on strategic planning and innovation initiatives.
