Robust Virtual Network Function Optimization Under Post-Failure Uncertainty: Classical & Quantum Computi

Abstract

The Service Function Chaining (SFC) failure is a rare event that comes with escalating unexpected costs. It is an overly simplistic assumption that during the installation of the Virtual Network Function (VNF) recovery instance, the network conditions impacting the cost of redeployment will remain unchanged. In this paper, we propose a deterministic optimization model using traditional Integer Linear Programming (ILP) that maneuvers the resource allocation for prior and post-failure SFC deployment. Afterwards, we design a robust optimization model that accounts for the uncertainty of the redeployment costs. As per the strong duality theorem, we derive the dual formulation of the robust optimization model for reduced computational complexity. Further along this line, we propose a quantum annealing-driven quadratic optimization (QUBO) model that demonstrates inherent robustness even without explicitly considering the uncertainty bounds of SFC redeployment costs. Extensive simulation studies demonstrate the superiority of robust solutions over deterministic approaches and explore the potential strengths of quantum annealing in terms of intrinsic resiliency. Although quantum computing is not yet ready to solve large-scale SFC deployment, it can support VNF services that demand ultra-low running time/real-time decision-making.