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

Current advancements in the design of the servicebased 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 delaytolerant 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.