Abstract
Service provider mobility involves large-scale deployments that serve millions of subscribers within a cluster of network functions (NFs). Even minor impairments in a deployed cluster can significantly impact a substantial number of subscribers. To address this issue, an advanced AI/ML-based failure prediction framework is proposed herein that is specifically tailored for mobility/telecom environments. This framework not only predicts impairments before they occur but also provides recommendations for actions that peer nodes can perform to prevent disruptions to existing subscribers from these potential impairments. With this framework, ML-based predictive signature is generated by NF source and shared with central entity, such that all directly and indirectly connected nodes can receive composite signatures from a central entity, including confidence scores of the NFs participating in a given service topology, along with their fully qualified domain name (FQDN), region, site, and host. This information will enable other NFs to select reliable peer nodes and avoid service disruptions by making use of predictive failure detection leveraging composite signatures and derived confidence score factor.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Anagani, Sujin; Pandey, Sudeep; Kunjan, Solomon Ayyankulankara; Mondal, Souvik; M Bhat, Ashok; and Katamreddy, Vijay, "FRAMEWORK UTILIZING AI/ML TO ENSURE GUARANTEED RELIABILITY IN 5G STANDALONE (SA) NETWORKS", Technical Disclosure Commons, (October 31, 2024)
https://www.tdcommons.org/dpubs_series/7489