Closed loop optimization of 5G network slices

Abstract

The rise of 5G promises ultra low latency, higher bandwidth and more connectivity. 5G network slicing will allow different customers with varying network requirements of latency, bandwidth, reliability and quality of service to co-exist on the same infrastructure. To enable rapid, reliable and scalable management and orchestration of these 5G network slices, there is a need for a 5G slicing lifecyle MANO with support for close loop optimization. In this work, we present the design of a 5G slicing lifecyle MANO framework that handles Day 0 operations of onboarding network functions and designing slice templates, Day 1 operations of zero-touch deployment of slices with intelligent decisions on optimal placement and Day 2 operations of slice monitoring and assurance based on Data and Analytics Function (DAF) and an Optimization Engine. We implement the proposed framework in IBM Cloud Pak for Network Automation (CP4NA). We demonstrate the closed loop optimization functionalities with the help of a case study and show proactive fault resolution with mean time to detect of 25 seconds and mean time to remediate of 15 seconds.