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题目 Solution for Virtual Network Embedding Problem based on Simulated Annealing Genetic Algorithm 作者 Jianjun YU 这篇文章的检索号等详细信息。 麻烦了。 |
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zzfelix
木虫 (小有名气)
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4楼2012-10-22 16:01:17
zzfelix
木虫 (小有名气)
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yjj691121: 金币+5, ★★★★★最佳答案, 谢谢 2012-10-25 09:20:41
yjj691121: 金币+5, ★★★★★最佳答案, 谢谢 2012-10-25 09:20:41
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Accession number: 20122415104335 Title: Solution for virtual network embedding problem based on simulated annealing genetic algorithm Authors: Yu, Jianjun1 Author affiliation: 1 Faculty of Information Engineering, Quzhou College of Technology, Quzhou, China Corresponding author: Yu, J. (yjj691121@126.com) Source title: 2012 2nd International Conference on Consumer Electronics, Communications and Networks, CECNet 2012 - Proceedings Abbreviated source title: Int. Conf. Consum. Electron., Commun. Networks, CECNet - Proc. Monograph title: 2012 2nd International Conference on Consumer Electronics, Communications and Networks, CECNet 2012 - Proceedings Issue date: 2012 Publication year: 2012 Pages: 579-582 Article number: 6201795 Language: Chinese ISBN-13: 9781457714153 Document type: Conference article (CA) Conference name: 2012 2nd International Conference on Consumer Electronics, Communications and Networks, CECNet 2012 Conference date: April 21, 2012 - April 23, 2012 Conference location: Three Gorges, China Conference code: 90025 Sponsor: IEEE Publisher: IEEE Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States Abstract: Assigning the resources of a virtual network to the components of a physical network, called Virtual Network Mapping, plays a central role in network virtualization. Since this problem is known to be NP-hard, previous research focused on designing heuristic-based algorithms which had clear separation between the node mapping and the link mapping phases. In this paper we propose a Simulated Annealing genetic algorithm to optimize node mapping, and the heuristic algorithm or multi-commodity flow algorithms to solve the link mapping. Our experimental evaluations show that the proposed algorithm increases the acceptance ratio and the revenue while decreasing the cost incurred by the substrate network in the long run. © 2012 IEEE. Number of references: 15 Main heading: Simulated annealing Controlled terms: Consumer electronics - Distributed computer systems - Genetic algorithms - Heuristic algorithms - Mapping Uncontrolled terms: Acceptance ratio - Annealing genetic algorithm - Embedding problems - Experimental evaluation - Multi-commodity flow - Network virtualization - Node mapping - NP-hard - Physical network - Substrate networks - Virtual networks Classification code: 921 Mathematics - 913 Production Planning and Control; Manufacturing - 902.1 Engineering Graphics - 723.1 Computer Programming - 723 Computer Software, Data Handling and Applications - 722.4 Digital Computers and Systems - 715 Electronic Equipment, General Purpose and Industrial DOI: 10.1109/CECNet.2012.6201795 Database: Compendex Compilation and indexing terms, © 2012 Elsevier Inc. |
2楼2012-10-22 11:20:17
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