AI-Empowered VNF Migration as a Cost-Loss-Effective Solution for Network Resilience

TL;DR

This paper introduces an AI-driven method for migrating virtual network functions in 5G MEC networks, balancing migration costs and outage losses by modeling user mobility and operational expenses.

Contribution

It presents a novel cost model and an AI-based approach for efficient VNF migration considering realistic user mobility and operational costs.

Findings

Effective reduction in outage-related losses

Cost-efficient VNF migration strategies

Adaptability to complex user mobility patterns

Abstract

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.