A Virtual Network Resource Allocation Model Based on Dynamic Resource Pricing

Xian-Cui Xiao,Xiang-Wei Zheng,Xin-Chun Cui,Yi Wei

doi:10.1109/access.2020.3020944

Xian-Cui Xiao, Xiang-Wei Zheng + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.3020944

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Abstract

The key to network virtualization technology is virtual network mapping, which has been proven to be an NP-hard problem. At present, the methods to solve the problem of virtual network mapping still have the following defects. Most of the existing literature is limited to static virtual network (VN) mapping and static linear resource pricing, which rely on peak allocation and don't meet the user dynamic resource requirements. Therefore, this paper proposes a virtual network resource allocation model based on dynamic resource pricing named GSO-RBFDM. Firstly, group search optimization (GSO) is used to optimize the node mapping scheme during the network mapping process to reduce the cost of network mapping. Secondly, a dynamic nonlinear resource pricing model is established, and genetic algorithm (GA) is used to more accurately search a low-cost network mapping path instead of the traditional Dijkstra algorithm. Finally, virtual network dynamic modeling is performed according to the user dynamic resource requirements, and radial basis function (RBF) is used to predict resource requirements to realize the dynamic resource allocation to users. Simulation results show that, compared with traditional virtual network mapping algorithms, GSO-RBFDM can not only realize dynamic resource allocation, but also show good performance in terms of acceptance rate, network cost, link pressure and average network revenue.

Highlights

With the continuous development of the availability of new technologies and practical applications, the problem of resource allocation in the emerging decentralized communication environment has become a research focus [1], [2]
Our research focuses on using radial basis function (RBF) to predict user requirement and reduce resource allocation redundancy, as well as dynamic pricing of resources and the use of genetic algorithm (GA) to optimize the mapping path
In view of the above research and application of intelligent algorithms and neural network algorithms and the research of VN mapping algorithm, we propose a virtual network resource allocation model based on dynamic resource pricing named group search optimization (GSO)-RBFDM

Summary

INTRODUCTION

With the continuous development of the availability of new technologies and practical applications, the problem of resource allocation in the emerging decentralized communication environment has become a research focus [1], [2]. It may be a promising method to use RBF algorithm to predict resource requirement and complete the dynamic allocation of network resources [12]–[14]. In the traditional network model, the pricing of link resources is static and does not consider the cost of physical nodes on the mapping path. It is undoubtedly the quickest and most effective to use the Dijkstra algorithm to find the shortest mapping path. (2) A dynamic and nonlinear resource pricing method is proposed, and GA is used to find a low-cost mapping path.

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CONCLUSION AND FUTURE WORK