An Efficient Benders Decomposition Approach for Optimal Large-Scale Network Slicing

TL;DR

This paper introduces an efficient Benders decomposition algorithm tailored for large-scale, NP-hard network slicing problems, enabling globally optimal solutions by decomposing the problem into function placement and traffic routing subproblems.

Contribution

The paper develops a customized Benders decomposition method with valid inequalities that significantly improves solution efficiency and quality for large-scale network slicing problems.

Findings

The proposed algorithm guarantees globally optimal solutions.

Valid inequalities accelerate convergence considerably.

The method outperforms existing algorithms in efficiency and solution quality.

Abstract

This paper considers the network slicing (NS) problem which attempts to map multiple customized virtual network requests to a common shared network infrastructure and allocate network resources to meet diverse service requirements. This paper proposes an efficient customized Benders decomposition algorithm for globally solving the large-scale NP-hard NS problem. The proposed algorithm decomposes the hard NS problem into two relatively easy function placement (FP) and traffic routing (TR) subproblems and iteratively solves them enabling the information feedback between each other, which makes it particularly suitable to solve large-scale problems. Specifically, the FP subproblem is to place service functions into cloud nodes in the network, and solving it can return a function placement strategy based on which the TR subproblem is defined; and the TR subproblem is to find paths connecting two nodes hosting two adjacent functions in the network, and solving it can either verify that the solution of the FP subproblem is an optimal solution of the original problem, or return a valid inequality to the FP subproblem that cuts off the current infeasible solution. The proposed algorithm is guaranteed to find the globally optimal solution of the NS problem. By taking the special structure of the NS problem into consideration, we successfully develop two families of valid inequalities that render the proposed algorithm converge much more quickly and thus much more efficient. Numerical results demonstrate that the proposed valid inequalities effectively accelerate the convergence of the decomposition algorithm, and the proposed algorithm significantly outperforms the existing algorithms in terms of both solution efficiency and quality.