Abstract
Elastic optical networks (EONs) virtualization can allow the virtual optical networks (VONs) to utilize all the physical resources of EONs, and can attain a high performance of the networks. However, the optimal scheme for VONs mapping should be determined. To tackle these challenges, a bi-level mathematical model is established. leader's and follower's objectives are to minimize energy consumption and the maximum index of used frequency slots, respectively. The bi-level mathematical model can determine the optimal schemes of VONs mapping. To solve the mathematical model effectively, a uniform design method is applied to generate initial population for the lower level problem. In addition, To solve the whole model effectively, a tailor-made encoding, population initialization, genetic operators and local search operator are designed. An efficient genetic algorithm with local search operator is proposed for the bi-level mathematical model. To evaluate the mathematical model and the designed algorithm, a large number of experiments are performed on three kinds of the widely used networks, and the experimental results indicate that the effectiveness of the proposed bi-level mathematical model and designed algorithms.
Highlights
The booming of internet based activities requires an high performance internet network [3], [41]
To solve the bi-level optimization model, we propose a genetic algorithm that is designed with two populations is proposed
BI-LEVEL MATHEMATICAL MODEL FOR virtual optical networks (VONs) MAPPING The challenge problem of virtual nodes mapping, routing and spectrum assignment in elastic optical networks (EONs) can be summarized as: virtual nodes in all VONs must be mapped to nodes in EONs
Summary
This work was supported in part by the National Natural Science Foundation of China under Grant 61472297 and Grant 61572391, in part by the Science and Technology Department of Henan Province under Grant 182102210132 and Grant 182102210537, in part by the Innovation Team Support Plan of University Science and Technology of Henan Province under Grant 19IRTSTHN014, in part by the Guangxi Natural Science Foundation of China under Grant 2016GXNSFAA380226, in part by the Guangxi Young and Middle-Aged Teachers’ Basic Ability Improvement Foundation of China under Grant 2017KY0866, in part by the Internet of Things and Big Data Application Research Foundation of Guilin University of Aerospace Technology under Grant KJPT201809, and in part by the Nanhu Scholars Program for Young Scholars of XYNU.
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