Towards Efficient Large-Scale Network Slicing: An LP Dynamic Rounding-and-Refinement Approach

TL;DR

This paper introduces a novel LP relaxation and a two-stage LP dynamic rounding-and-refinement algorithm for large-scale network slicing, achieving better solution quality with polynomial complexity.

Contribution

The paper presents a new compact LP relaxation and an efficient algorithm that improves solution quality and scalability for large-scale network slicing problems.

Findings

The proposed algorithm outperforms existing methods in solution quality.

It has polynomial worst-case complexity, suitable for large-scale problems.

Numerical results confirm its effectiveness and efficiency.

Abstract

In this paper, we propose an efficient algorithm for 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 service requirements. The problem has been formulated as a mixed integer linear programming (MILP) formulation in the literature. We first propose a novel linear programming (LP) relaxation of the MILP formulation. We show that compared with a natural LP relaxation of the MILP formulation, the novel LP relaxation is much more compact in terms of smaller numbers of variables and constraints, and much stronger in terms of providing a better LP bound, which makes it particularly suitable to be embedded in an LP relaxation based algorithm. Then we design an efficient two-stage LP dynamic rounding-and-refinement algorithm based on this novel LP relaxation. In the first stage, the proposed algorithm uses an LP dynamic rounding procedure to place the virtual network functions of all services into cloud nodes while taking traffic routing of all services into consideration; in the second stage, the proposed algorithm uses an iterative LP refinement procedure to obtain a solution for traffic routing of all services with their end-to-end delay constraints being satisfied. Compared with the existing algorithms which either have an exponential complexity or return a low-quality solution, our proposed algorithm achieves a better trade-off between the solution quality and the computational complexity. In particular, the worst-case complexity of our proposed algorithm is polynomial, which makes it suitable for solving large-scale problems. Numerical results demonstrate the effectiveness and efficiency of our proposed algorithm.