# Dynamic Scaling of Virtualized, Distributed Service Chains: A Case Study of IMS

## TL;DR

This paper presents a system for dynamically scaling geo-distributed virtualized network service chains, specifically for IMS, to improve cost efficiency and performance in mobile networks.

## Contribution

It introduces a novel management system for coordinating VNF deployment across multiple data centers, combining proactive and reactive scaling strategies for IMS service chains.

## Key findings

- Effective scaling reduces operational costs.
- System adapts to changing network demands.
- Validated through real-world experiments.

## Abstract

The emerging paradigm of network function virtualization advocates deploying virtualized network functions (VNF) on standard virtualization platforms for significant cost reduction and management flexibility. There have been system designs for managing dynamic deployment and scaling of VNF service chains within one cloud data center. Many real-world network services involve geo-distributed service chains, with prominent examples of mobile core networks and IMSs (IP Multimedia Subsystems). Virtualizing these service chains requires efficient coordination of VNF deployment across different geo-distributed data centers over time, calling for new management system design. This paper designs a dynamic scaling system for geo-distributed VNF service chains, using the case of an IMS. IMSs are widely used subsystems for delivering multimedia services among mobile users in a 3G/4G network, whose virtualization has been broadly advocated in the industry for reducing cost, improving network usage efficiency and enabling dynamic network topology reconfiguration for performance optimization. Our scaling system design caters to key control-plane and data-plane service chains in an IMS, combining proactive and reactive approaches for timely, cost-effective scaling of the service chains. We evaluate our system design using real-world experiments on both emulated platforms and geo-distributed clouds.