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

arXiv: https://arxiv.org/abs/2101.09343 · HTML: https://mlsanity.com/paper/2101.09343/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [5G Island for Network Resilience and Autonomous Failsafe Operations](https://mlsanity.com/paper/1805.01715/) (2018)

## Built on by (most cited first, 1 of 1)

- [User Trajectory Prediction Unifying Global and Local Temporal Information](https://mlsanity.com/paper/2508.02161/) (2025)

## Abstract (arXiv)

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.