AI-Empowered VNF Migration as a Cost-Loss-Effective Solution for Network Resilience
This addresses network resilience for 5G mobile networks by optimizing VNF migration, though it appears incremental as it builds on existing migration concepts with new modeling.
The paper tackles the problem of balancing operations cost and potential loss in virtual network function (VNF) migration for network resilience in 5G networks, proposing a novel cost model and AI-empowered approach that minimizes the sum of cost and loss while handling realistic user mobility patterns.
With a wide deployment of Multi-Access Edge Computing (MEC) in the Fifth Generation (5G) mobile networks, virtual network functions (VNF) can be flexibly migrated between difference locations, and therewith significantly enhances the network resilience to counter the degradation in quality of service (QoS) due to network function outages. A balance has to be taken carefully, between the loss reduced by VNF migration and the operations cost generated thereby. To achieve this in practical scenarios with realistic user behavior, it calls for models of both cost and user mobility. This paper proposes a novel cost model and a AI-empowered approach for a rational migration of stateful VNFs, which minimizes the sum of operations cost and potential loss caused by outages, and is capable to deal with the complex realistic user mobility patterns.