Check out CEO William Bain’s article about how network digital twins (NDTs) create a real-time virtual mirror of live telecom networks, enabling operators to visualize, predict, and optimize performance.

NDTs use live telemetry, predictive modeling, and AI to model network conditions and adjust operational parameters in real time. Integrated machine learning can help predict emerging issues by identifying subtle patterns in device telemetry, while generative AI optimizes performance and reduces energy use under dynamic workloads.

ScaleOut Software has pioneered a software technology called in-memory computing, which can serve as a key enabler to making NDTs practical at scale. By hosting millions of in-memory objects across a scalable cluster of servers and processing telemetry in milliseconds, it delivers the responsiveness needed to emulate very large telecom networks. This powerful architecture allows NDTs to capture interactions among components and dynamically adjust network parameters, ensuring operators can keep pace with growing telecom demands.