• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于软件定义网络的分段路由优化路由算法。

An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks.

机构信息

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

出版信息

Sensors (Basel). 2018 Dec 22;19(1):49. doi: 10.3390/s19010049.

DOI:10.3390/s19010049
PMID:30583564
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6339048/
Abstract

Software-defined networks (SDNs) are improving the controllability and flexibility of networks as an innovative network architecture paradigm. Segment routing (SR) exploits an end-to-end logical path and is composed of a sequence of segments as an effective routing strategy. Each segment is represented by a middle point. The combination of SR and SDN can meet the differentiated business needs of users and can quickly deploy applications. In this paper, we propose two routing algorithms based on SR in SDN. The algorithms aim to save the cost of the path, alleviate the congestion of networks, and formulate the selection strategy by comprehensively evaluating the value of paths. The simulation results show that compared with existing algorithms, the two proposed algorithms can effectively reduce the consumption of paths and better balance the load of the network. Furthermore, the proposed algorithms take into account the preferences of users, actualize differentiated business networks, and achieve a larger comprehensive evaluation value of the path compared with other algorithms.

摘要

软件定义网络(SDN)作为一种创新的网络架构范例,提高了网络的可控性和灵活性。分段路由(SR)利用端到端的逻辑路径,并由一系列段组成,是一种有效的路由策略。每个段都由一个中间点表示。SR 和 SDN 的结合可以满足用户的差异化业务需求,并能够快速部署应用程序。在本文中,我们提出了两种基于 SDN 中 SR 的路由算法。这些算法旨在节省路径成本,缓解网络拥塞,并通过综合评估路径的价值来制定选择策略。仿真结果表明,与现有算法相比,所提出的两种算法能够有效地降低路径的消耗,更好地平衡网络的负载。此外,所提出的算法考虑了用户的偏好,实现了差异化的业务网络,并与其他算法相比,实现了更大的路径综合评估值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/bcfcce39cd7e/sensors-19-00049-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/208dcb2d137f/sensors-19-00049-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/edd7de2e9f7d/sensors-19-00049-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/b257b0a7ffc3/sensors-19-00049-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/02110bbfa137/sensors-19-00049-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/7cbb2f2e3faa/sensors-19-00049-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/42e557828815/sensors-19-00049-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/fde5bdb49f10/sensors-19-00049-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/98f26ffbf869/sensors-19-00049-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/8bd43ba701fa/sensors-19-00049-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/bcfcce39cd7e/sensors-19-00049-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/208dcb2d137f/sensors-19-00049-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/edd7de2e9f7d/sensors-19-00049-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/b257b0a7ffc3/sensors-19-00049-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/02110bbfa137/sensors-19-00049-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/7cbb2f2e3faa/sensors-19-00049-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/42e557828815/sensors-19-00049-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/fde5bdb49f10/sensors-19-00049-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/98f26ffbf869/sensors-19-00049-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/8bd43ba701fa/sensors-19-00049-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df92/6339048/bcfcce39cd7e/sensors-19-00049-g010.jpg

相似文献

1
An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks.基于软件定义网络的分段路由优化路由算法。
Sensors (Basel). 2018 Dec 22;19(1):49. doi: 10.3390/s19010049.
2
A fuzzy delay-bandwidth guaranteed routing algorithm for video conferencing services over SDN networks.一种用于软件定义网络(SDN)上视频会议服务的模糊延迟 - 带宽保证路由算法。
Multimed Tools Appl. 2023 Jan 23:1-30. doi: 10.1007/s11042-023-14349-6.
3
An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization.一种无线传感器网络中的高效混合路由算法:结合跳数最小化的蚁群优化算法。
Sensors (Basel). 2018 Mar 29;18(4):1020. doi: 10.3390/s18041020.
4
A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.一种优化软件定义网络的流路由和轮询交换机选择的方案。
PLoS One. 2015 Dec 21;10(12):e0145437. doi: 10.1371/journal.pone.0145437. eCollection 2015.
5
Inter-domain routing based on simulated annealing algorithm in optical mesh networks.光网状网络中基于模拟退火算法的域间路由
Opt Express. 2004 Jul 12;12(14):3095-107. doi: 10.1364/opex.12.003095.
6
Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT).基于应用感知的软件定义网络的物联网迭代可重构路由协议
Sensors (Basel). 2020 Jun 22;20(12):3521. doi: 10.3390/s20123521.
7
SACFIR: SDN-Based Application-Aware Centralized Adaptive Flow Iterative Reconfiguring Routing Protocol for WSNs.SACFIR:用于无线传感器网络的基于软件定义网络的应用感知集中式自适应流迭代重配置路由协议
Sensors (Basel). 2017 Dec 13;17(12):2893. doi: 10.3390/s17122893.
8
An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks.基于均衡策略的无线传感器网络路由优化算法。
Sensors (Basel). 2018 Oct 16;18(10):3477. doi: 10.3390/s18103477.
9
Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks.低轨卫星网络中的快速收敛强化学习路由。
Sensors (Basel). 2023 May 29;23(11):5180. doi: 10.3390/s23115180.
10
Efficient path routing strategy for flows with multiple priorities on scale-free networks.无标度网络上具有多个优先级流的高效路径路由策略
PLoS One. 2017 Feb 15;12(2):e0172035. doi: 10.1371/journal.pone.0172035. eCollection 2017.

本文引用的文献

1
An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm.基于外部档案的多目标粒子群优化算法。
IEEE Trans Cybern. 2017 Sep;47(9):2794-2808. doi: 10.1109/TCYB.2017.2710133. Epub 2017 Jun 12.
2
An Adaptive Multiobjective Particle Swarm Optimization Based on Multiple Adaptive Methods.基于多种自适应方法的自适应多目标粒子群优化算法。
IEEE Trans Cybern. 2017 Sep;47(9):2754-2767. doi: 10.1109/TCYB.2017.2692385. Epub 2017 Apr 17.