An Efficient Benders Decomposition Approach for Optimal Large-Scale Network Slicing
| Autor: | Chen, Wei-Kun, Wu, Zheyu, Zhang, Rui-Jin, Liu, Ya-Feng, Dai, Yu-Hong, Luo, Zhi-Quan |
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| Rok vydání: | 2023 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 14 pages, 10 figures, accepted for publication in the IEEE Transactions on Signal Processing; for the conference version, see arXiv:2306.15247v1 |
| Databáze: | arXiv |
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