Telemetry Knowledge Distributed Processing for Network Digital Twins and Network Resilience

A network digital twin (NDT) is a virtual object that mirrors a network system (NS) so that a control and management system (CMS) may identify present and potential failures using artificial intelligence methods, resulting in enhanced NS resilience. In this study, we propose a method of enhancing an NDT by generating and transmitting telemetry knowledge objects (TKOs) instead of raw monitoring data. TKOs enforce data correction, provide rich information, and are inherently adaptable. We also apply the telemetry knowledge distributed processing (TKDP) approach to the CMS so that each NS element processes its own measurements to generate and transmit TKOs, thus avoiding the transmission of meaningless, wrongly formed, or incomplete data. Moreover, distributing the processing capacity allows the target NDTs to reflect more complex NS states than when using centralized processing. To maximize the impact of TKOs and TKDPs, we followed the current standardization specifications for network telemetry and NDT construction. Finally, we demonstrated the benefits of TKOs and TKDPs by evaluating the fidelity of an NDT that matches a typical scenario. We showed that the resultant NDT fidelity is much higher when using TKO and TKDP than when relying on current alternatives.