| Paper ID | SPCOM-1.5 |
| Paper Title |
AN EFFICIENT LINEAR PROGRAMMING ROUNDING-AND-REFINEMENT ALGORITHM FOR LARGE-SCALE NETWORK SLICING PROBLEM |
| Authors |
Wei-Kun Chen, Beijing Institute of Technology, China; Ya-Feng Liu, Yu-Hong Dai, Chinese Academy of Sciences, China; Zhi-Quan Luo, Shenzhen Research Institute of Big Data and The Chinese University of Hong Kong, China |
| Session | SPCOM-1: Signal Processing for Networks |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing for Communications and Networking: [SPCN-NETW] Networks and Network Resource allocation |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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 service requirements, and propose an efficient two-stage algorithm for solving this NP-hard problem. In the first stage, the proposed algorithm uses an iterative linear programming (LP) 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 solution quality and 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. |
