Robust Network Function Virtualization

TL;DR

This paper introduces evaluation metrics and algorithms for enhancing the reliability of network function virtualization (NFV) under node failures, enabling more robust and flexible network service deployment.

Contribution

It presents new reliability metrics, pseudo-polynomial algorithms, approximation algorithms, and MILP-based exact solutions for robust NFV and service function chaining problems.

Findings

Algorithms effectively manage robust NFV in large networks.

Proposed solutions improve NFV reliability under node failures.

Computational results validate the efficiency of the approaches.

Abstract

Network function virtualization (NFV) enables on-demand network function (NF) deployment providing agile and dynamic network services. Through an evaluation metric that quantifies the minimal reliability among all NFs for all demands, service providers and operators may better facilitate flexible NF service recovery and migration, thus offer higher service reliability. In this paper, we present evaluation metrics on NFV reliability and solution approaches to solve robust NFV under random NF-enabled node failure(s). We demonstrate how to construct an auxiliary NF-enabled network and its mapping onto the physical substrate network. With constructed NF-enabled network, we develop pseudo-polynomial algorithms to solve the robust NF and SFC $s-t$ path problems -- subproblems of robust NFV. We also present approximation algorithms for robust NFV with the SFC-Fork as the NF forwarding graph. Furthermore, we propose exact solution approaches via mixed-integer linear programming (MILP) under the general setting. Computational results show that our proposed solution approaches are capable of managing robust NFV in a large-size network.