Abstract
The management of complex telecommunications networks can be challenged by difficulties in predicting the impact of component failures and the risks of testing automations on a live system. Systems and methods are described that can address these challenges by creating and maintaining a graph-based digital representation, such as a digital twin, of an operational network that can be synchronized with real-time data. Within this digital twin, for example, an autonomous agent can perform synthetic fault injections to simulate a wide range of potential failures and stressors. The system can analyze the effects of these simulated faults to predict potential service-level agreement violations. Upon predicting a potential violation, a remediation strategy can be generated and validated within the digital twin's environment before its potential deployment, which may enable a proactive and predictive approach to network maintenance.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Chandra, Mohit and Mukherjee, Amrita, "Autonomous Stress Testing in a Network Digital Twin for Predictive Remediation", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10774