A Fair Virtual Network Embedding Algorithm

2014 ◽  
Vol 1049-1050 ◽  
pp. 1454-1457
Author(s):  
Cong Wang ◽  
Ying Yuan ◽  
Cong Wan
Keyword(s):  
Disperse Particle ◽  
Virtual Network ◽  
Virtual Network Embedding ◽  
Network Embedding ◽  
Transformation Mechanism ◽  
Network Resource ◽  
Topological Transformation ◽  
Swarm Optimization ◽  
Transformation Procedure ◽  
Substrate Network

Efficient network resource utilization is crucial in Virtual Network Embedding (VNE) problem. The diversity of virtual topologies belong to various services providers (SPs) severely affects the efficiency of VNE algorithms and fairness between SPs. This paper proposes a fair VNE algorithm with a topological transformation mechanism. Such mechanism will transform virtual topology to reduce complexity. Then the algorithm will felicitously map the virtual network onto substrate network through a Disperse Particle Swarm Optimization (DPSO) based process. Simulation results show that due to the topological transformation procedure the algorithm can achieve more fairness than traditional VNE algorithms.

scholarly journals An Edge-Based Approach for Virtual Network Embedding Based on the Graph Edit Distance

2021 ◽  
Author(s):  
Ze Xi Xu ◽  
Lei Zhuang ◽  
Meng Yang He ◽  
Si Jin Yang ◽  
Yu Song ◽  
...  
Keyword(s):  
Edit Distance ◽  
Virtual Network ◽  
Cost Ratio ◽  
Virtual Network Embedding ◽  
Network Embedding ◽  
Graph Edit Distance ◽  
Network Resources ◽  
Network Resource ◽  
Distance Method ◽  
Edge Based

Abstract Virtualization and resource isolation techniques have enabled the efficient sharing of networked resources. How to control network resource allocation accurately and flexibly has gradually become a research hotspot due to the growth in user demands. Therefore, this paper presents a new edge-based virtual network embedding approach to studying this problem that employs a graph edit distance method to accurately control resource usage. In particular, to manage network resources efficiently, we restrict the use conditions of network resources and restrict the structure based on common substructure isomorphism and an improved spider monkey optimization algorithm is employed to prune redundant information from the substrate network. Experimental results showed that the proposed method achieves better performance than existing algorithms in terms of resource management capacity, including energy savings and the revenue-cost ratio.

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