Optimal QoS-Aware Network Slicing for Service-Oriented Networks with Flexible Routing

TL;DR

This paper introduces a novel optimization framework for network slicing that efficiently allocates resources while ensuring QoS requirements, using MILP formulations that are computationally feasible and outperform existing methods.

Contribution

It proposes a MILP-based approach for QoS-aware network slicing with flexible routing, overcoming the computational challenges of nonlinear formulations.

Findings

MILP formulation is computationally efficient and scalable.

Proposed models outperform existing solutions in resource utilization.

Flexible routing provides end-to-end delay and reliability guarantees.

Abstract

In this paper, we consider the network slicing problem which attempts to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and allocate network resources to meet diverse quality of service (QoS) requirements. We first propose a mixed integer nonlinear program (MINLP) formulation for this problem that optimizes the network resource consumption while jointly considers QoS requirements, flow routing, and resource budget constraints. In particular, the proposed formulation is able to flexibly route the traffic flow of the services on multiple paths and provide end-to-end (E2E) delay and reliability guarantees for all services. Due to the intrinsic nonlinearity, the MINLP formulation is computationally difficult to solve. To overcome this difficulty, we then propose a mixed integer linear program (MILP) formulation and show that the two formulations and their continuous relaxations are equivalent. Different from the continuous relaxation of the MINLP formulation which is a nonconvex nonlinear programming problem, the continuous relaxation of the MILP formulation is a polynomial time solvable linear programming problem, which makes the MILP formulation much more computationally solvable. Numerical results demonstrate the effectiveness and efficiency of the proposed formulations over existing ones.