Design and Analysis of Dynamic Auto Scaling Algorithm (DASA) for 5G Mobile Networks

TL;DR

This paper introduces DASA, an auto-scaling algorithm for 5G networks that balances performance and operational costs, validated through analytical modeling and simulations.

Contribution

It proposes a novel VNF auto-scaling algorithm considering legacy equipment, with an analytical model to evaluate cost-performance tradeoffs.

Findings

DASA reduces operation costs while maintaining latency bounds.

The queueing model effectively predicts cost and performance.

Analytical model accelerates evaluation without extensive deployment.

Abstract

Network Function Virtualization (NFV) enables mobile operators to virtualize their network entities as Virtualized Network Functions (VNFs), offering fine-grained on-demand network capabilities. VNFs can be dynamically scale-in/out to meet the performance requirements for future 5G networks. However, designing an auto-scaling algorithm with low operation cost and low latency while considering the capacity of legacy network equipment is a challenge. In this paper, we propose a VNF Dynamic Auto Scaling Algorithm (DASA) considering the tradeoff between performance and operation cost. We also develop an analytical model to quantify the tradeoff and validate the analysis through extensive simulations. The system is modeled as a queueing model while legacy network equipment is considered as a reserved block of servers. The VNF instances are powered on and off according to the number of job requests. The results show that the proposed DASA can significantly reduce operation cost given the latency upper-bound. Moreover, the models provide a quick way to evaluate the cost-performance tradeoff without wide deployment, which can save cost and time.