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영문 논문지

홈 홈 > 연구문헌 > 영문 논문지 > TIIS (한국인터넷정보학회)

TIIS (한국인터넷정보학회)

Current Result Document : 6 / 27 이전건 이전건   다음건 다음건

한글제목(Korean Title) Cognitive Virtual Network Embedding Algorithm Based on Weighted Relative Entropy
영문제목(English Title) Cognitive Virtual Network Embedding Algorithm Based on Weighted Relative Entropy
저자(Author) Yuze Su   Xiangru Meng   Zhiyuan Zhao   Zhentao Li  
원문수록처(Citation) VOL 13 NO. 04 PP. 1845 ~ 1865 (2019. 04)
한글내용
(Korean Abstract)
영문내용
(English Abstract)
Current Internet is designed by lots of service providers with different objects and policies which make the direct deployment of radically new architecture and protocols on Internet nearly impossible without reaching a consensus among almost all of them. Network virtualization is proposed to fend off this ossification of Internet architecture and add diversity to the future Internet. As an important part of network virtualization, virtual network embedding (VNE) problem has received more and more attention. In order to solve the problems of large embedding cost, low acceptance ratio (AR) and environmental adaptability in VNE algorithms, cognitive method is introduced to improve the adaptability to the changing environment and a cognitive virtual network embedding algorithmbased on weighted relative entropy (WRE-CVNE) is proposed in this paper. At first, the weighted relative entropy (WRE) method is proposed to select the suitable substrate nodes and paths in VNE. In WRE method, the ranking indicators and their weighting coefficients are selected to calculate the node importance and path importance. It is the basic of the WRE-CVNE. In virtual node embedding stage, the WRE method and breadth first search (BFS) algorithm are both used, and the node proximity is introduced into substrate node ranking to achieve the joint topology awareness. Finally, in virtual link embedding stage, the CPU resource balance degree, bandwidth resource balance degree and path hop counts are taken into account. The path importance is calculated based on the WRE method and the suitable substrate path is selected to reduce the resource fragmentation. Simulation results show that the proposed algorithm can significantly improve AR and the long-term average revenue to cost ratio (LTAR/CR) by adjusting the weighting coefficients in VNE stage according to the network environment. We also analyze the impact of weighting coefficient on the performance of the WRE-CVNE. In addition, the adaptability of the WRE-CVNE is researched in three different scenarios and the effectiveness and efficiency of theWRE-CVNE are demonstrated.
키워드(Keyword) Virtual Network Embedding   Cognitive   Weighted Relative Entropy   Joint Topology Awareness   Breadth First Search  
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