sdn云环境下虚拟网络嵌入的驱动型现代投资组合理论

IF 0.9 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Scalable Computing-Practice and Experience Pub Date : 2022-12-24 DOI:10.12694/scpe.v23i4.2010
Abderrahim Bouchair, Belabbas Yagoubi, Sid Ahmed Makhlouf
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引用次数: 0

摘要

网络虚拟化(Network virtualization, NV)是在大规模环境下共享计算资源和改进业务部署的迫切需要下系统发展起来的技术。虚拟网络嵌入(VNE)是一种完善的技术,用于加强虚拟网络过程,为特定案例研究提供专门的实现。在云计算中,软件定义网络(SDN)的集成已被证明是对主要云实用程序的实际支持。作为回报,支持sdn的云为基于网络的服务提供了创新的部署技术,这增加了有效整合解决VNE问题的新网络管理策略的机会。在本文中,作者提出了一种将现代投资组合理论(MPT)转变为VNE方法的方法,该方法可以最佳地解决数据中心网络(dcn)中资源的选择和排序问题。结果分析表明,VNE方法在接受率、运行时间和衬底资源利用率方面优于类似方法。
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A Driven Modern Portfolio Theory for Virtual Network Embedding in SDN-Enabled Cloud
Network virtualization (NV) has evolved systematically through the urge to share computing resources and improve service deployment in a large-scale environment. Virtual network embedding (VNE) is a well-established technology applied to reinforce the NV process, providing a devoted implementation for a particular case study. In cloud computing, integration of software-defined networking (SDN) has proved to be a practical support to the principal cloud utilities. In return, the SDN-enabled cloud offers innovative deployment techniques for network-based services, which increase the opportunity to efficiently incorporate new network management policies that solve the VNE problem. In this paper, the authors proposed a transition of modern portfolio theory (MPT) into a VNE approach that optimally addresses the selection and ranking of resources in data center networks (DCNs). Results analysis demonstrates the VNE approach's better performance versus similar methods in terms of acceptance ratio, runtime, and substrate resource utilization.
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来源期刊
Scalable Computing-Practice and Experience
Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.00
自引率
0.00%
发文量
10
期刊介绍: The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.
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