Abstract
The optimal embedding problem of virtual network requests, which satisfies nodes and link constraints, is a NP-hard problem. Heuristic algorithms solve the problem with the mathematical model optimization, but it fails to consider the influence of the virtual network embedding node itself on the optimal solution. So the cellular automata genetic mechanism is introduced into the problem, and the virtual network embedding algorithm based on cellular genetic algorithm (VNE-CGA) has been proposed. VNE-CGA uses the cellular automata to model the node, and replaces the "B4567/S1234" rule with the crossover operation in genetic algorithm. Through learning from neighbours to guide the individual's optimization process, VNECGA improves the inherent defects of traditional genetic algorithm. The experimental results show that the request acceptance ratio and the long-term average revenue increase about 5% and 12%.
Highlights
With the expansion of the Internet and the growth of new services, the traditional network structure tends to be "rigid", and network virtualization provides strong support for the innovation and sustainable development of the network [1, 2]
Ref. [6] proposed a virtual network embedding algorithm based on ant colony algorithm, which uses ant colony iteration and intelligence to solve this optimization problem
The primary research is that cellular genetic algorithm was used to solve the virtual network embedding optimization problem with nodes and links constraints
Summary
With the expansion of the Internet and the growth of new services, the traditional network structure tends to be "rigid", and network virtualization provides strong support for the innovation and sustainable development of the network [1, 2]. The core idea of the network virtualization is constructing its own virtual network (VN) for different services on the same physical network [3] It is a NP-hard problem to provide the underlying physical network resources to virtual network requests on demand [4]. [6] proposed a virtual network embedding algorithm based on ant colony algorithm, which uses ant colony iteration and intelligence to solve this optimization problem. [8] proposed an iterative optimization embedding solution VNE-PSO-GA, which merges genetic algorithm and particle swarm optimization algorithm together, to solve the local optimal problem.
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