Closed loop optimization of 5G network slices

Abstract

The rapid adoption of Software Defined Networking (SDN) and Network Function Virtualization (NFV) in 5G telecommunication networks has made network slicing possible, where different customers with varying network requirements of latency, bandwidth, reliability and quality of service can co-exist with isolated virtual networks on the same infrastructure. To enable rapid, reliable and scalable management and orchestration (MANO) of these 5G network slices, there is a need for embedding intelligence and optimization into all aspects of lifecycle management including slice planning, automated deployment and operational assurance. In this work, we present the design of a 5G slicing lifecycle 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 using a Data and Analytics Function and an optimization engine. The framework can handle a wide range of use cases including optimal resource allocation to different sites, dynamic placement of network functions, and revenue or performance optimization. We present closed-loop placement optimization upon a failure as a case study and show proactive resolution with mean time to detect of 14.7s and mean time to remediate of 15s.