VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management

IF 3.3 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Thiruvenkadam Srinivasan, Sujitha Venkatapathy, Han-Gue Jo, In-Ho Ra
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引用次数: 0

Abstract

Network slicing is widely regarded as the most critical technique for allocating network resources to varied user needs in 5G networks. A Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two extensively used strategies for slicing the physical infrastructure according to use cases. The most efficient use of virtual networks is realized by the application of optimal resource allocation algorithms. Numerous research papers on 5G network resource allocation focus on network slicing or on the best resource allocation for the sliced network. This study uses network slicing and optimal resource allocation to achieve performance optimization using requirement-based network slicing. The proposed approach includes three phases: (1) Slice Creation by Machine Learning methods (SCML), (2) Slice Isolation through Resource Allocation (SIRA) of requests via a multi-criteria decision-making approach, and (3) Slice Management through Resource Transfer (SMART). We receive a set of Network Service Requests (NSRs) from users. After receiving the NSRs, the SCML is used to form slices, and SIRA and SMART are used to allocate resources to these slices. Accurately measuring the acceptance ratio and resource efficiency helps to enhance overall performance. The simulation results show that the SMART scheme can dynamically change the resource allocation according to the test conditions. For a range of network situations and Network Service Requests (NSRs), the performance benefit is studied. The findings of the simulation are compared to those of the literature in order to illustrate the usefulness of the proposed work.
支持vnf的5G网络编排框架,用于切片创建、隔离和管理
网络切片被广泛认为是5G网络中为不同用户需求分配网络资源的最关键技术。软件定义网络(SDN)和网络功能虚拟化(NFV)是根据用例对物理基础设施进行切片的两种广泛使用的策略。通过最优资源分配算法的应用,实现了虚拟网络的最有效利用。大量关于5G网络资源分配的研究论文集中在网络切片或切片网络的最佳资源分配上。本研究使用网络切片和最优资源分配来实现基于需求的网络切片的性能优化。提出的方法包括三个阶段:(1)通过机器学习方法(SCML)创建片,(2)通过多标准决策方法对请求进行资源分配(SIRA)的片隔离,以及(3)通过资源转移(SMART)进行片管理。我们从用户那里收到一组网络服务请求(nsr)。收到nsr后,使用SCML组成分片,并使用SIRA和SMART为这些分片分配资源。准确测量可接受率和资源效率有助于提高整体性能。仿真结果表明,SMART方案可以根据测试条件动态改变资源分配。针对一系列网络情况和网络服务请求(nsr),研究了性能效益。模拟的结果与文献的结果进行了比较,以说明所提出工作的有用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Sensor and Actuator Networks
Journal of Sensor and Actuator Networks Physics and Astronomy-Instrumentation
CiteScore
7.90
自引率
2.90%
发文量
70
审稿时长
11 weeks
期刊介绍: Journal of Sensor and Actuator Networks (ISSN 2224-2708) is an international open access journal on the science and technology of sensor and actuator networks. It publishes regular research papers, reviews (including comprehensive reviews on complete sensor and actuator networks), and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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