Revamping the Resilience and High Availability of 5G Core for 6G Ready Network Slices

Current advancements in the design of the service-based architecture of 5G Core (5GC) are flexible to be deployed with Network Function Virtualization (NFV) in the form of network slices. While some slices tolerate the delay and some do not, this versatility of slice services imposes challenges to Mobile Network Operators in designing a robust, scalable, and yet resilient 5GC. Therefore, in this work, we build a self-resilient 5GC that not only addresses the scheduling requirements per slice type but also yields service satisfaction. Precisely, we build it as a combination of large Integer Linear Programming based Column Generation (CG) and Artificial Intelligence based Deep Learning in a Closed Loop Automation with a Self Organizing Network paradigm. During unexpected situations of overload and failures of serving instances, the proposed CG technique in the work improves the scheduling and serving of control plane user requests by dropping 15% lesser user requests in delay-tolerant slices and 5% lesser in delay-sensitive slices, than the existing state-of-the-art solutions. With 95% learning accuracy, the Deep Learning-based Long Short Term Memory (LSTM) method predicts the availability of serving instances of the Network Functions (NFs) of the 5GC. Overall, by deriving the optimal number of serving instances for each NF of the 5GC, this blend of CG and AI in the 5GC not only contributes to efficient scaling and reconfiguration of the slices but also improves the resilience and High Availability of the 5GC. We believe that this proposed work is a highly promising solution for building Cognitive Autonomous Networks (CAN) of core side slices for upcoming Beyond 5G (B5G) and 6G in both cloud and edge deployments.