Quantum-Assisted Joint Virtual Network Function Deployment and Maximum Flow Routing for Space Information Networks

TL;DR

This paper introduces a quantum-assisted optimization framework for joint virtual network function deployment and flow routing in space information networks, enhancing efficiency and scalability for global communication services.

Contribution

It proposes a hybrid quantum-classical algorithm for solving complex joint optimization problems in space networks, leveraging quantum computing for improved performance.

Findings

The quantum-assisted algorithm outperforms classical methods in efficiency.

The joint optimization significantly improves data processing capacity.

Quantum techniques enable faster solutions for intractable MILP problems.

Abstract

Network function virtualization (NFV)-enabled space information network (SIN) has emerged as a promising method to facilitate global coverage and seamless service. This paper proposes a novel NFV-enabled SIN to provide end-to-end communication and computation services for ground users. Based on the multi-functional time expanded graph (MF-TEG), we jointly optimize the user association, virtual network function (VNF) deployment, and flow routing strategy (U-VNF-R) to maximize the total processed data received by users. The original problem is a mixed-integer linear program (MILP) that is intractable for classical computers. Inspired by quantum computing techniques, we propose a hybrid quantum-classical Benders' decomposition (HQCBD) algorithm. Specifically, we convert the master problem of the Benders' decomposition into the quadratic unconstrained binary optimization (QUBO) model and solve it with quantum computers. To further accelerate the optimization, we also design a multi-cut strategy based on the quantum advantages in parallel computing. Numerical results demonstrate the effectiveness and efficiency of the proposed algorithm and U-VNF-R scheme.