Reducing Service Deployment Cost Through VNF Sharing

TL;DR

This paper introduces FlexShare, an algorithm that optimizes VNF sharing and prioritization to reduce deployment costs in mobile networks while ensuring service performance, demonstrating near-optimal results with real-world data.

Contribution

The paper presents a novel polynomial-time algorithm, FlexShare, for dynamic VNF sharing and priority management that guarantees near-optimal cost reduction while meeting KPIs.

Findings

FlexShare achieves near-optimal cost savings in VNF sharing.

Dynamic priority management improves service performance.

Real-world tests show FlexShare outperforms baseline methods.

Abstract

Thanks to its computational and forwarding capabilities, the mobile network infrastructure can support several third-party ("vertical") services, each composed of a graph of virtual (network) functions (VNFs). Importantly, one or more VNFs are often common to multiple services, thus the services deployment cost could be reduced by letting the services share the same VNF instance instead of devoting a separate instance to each service. By doing that, however, it is critical that the target KPI (key performance indicators) of all services are met. To this end, we study the VNF sharing problem and make decisions on (i) when sharing VNFs among multiple services is possible, (ii) how to adapt the virtual machines running the shared VNFs to the combined load of the assigned services, and (iii) how to prioritize the services traffic within shared VNFs. All decisions aim to minimize the cost for the mobile operator, subject to requirements on end-to-end service performance, e.g., total delay. Notably, we show that the aforementioned priorities should be managed dynamically and vary across VNFs. We then propose the FlexShare algorithm to provide near-optimal VNF-sharing and priority assignment decisions in polynomial time. We prove that FlexShare is within a constant factor from the optimum and, using real-world VNF graphs, we show that it consistently outperforms baseline solutions.