• 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

一种用于软件定义广域网分析的流体流动模型。

A fluid flow model for the software defined wide area networks analysis.

作者信息

Marszałek Karol, Domański Adam

机构信息

Department of Distributed Systems and Informatic Devices, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland.

出版信息

Sci Rep. 2025 Jan 29;15(1):3713. doi: 10.1038/s41598-025-88162-6.

DOI:10.1038/s41598-025-88162-6
PMID:39880972
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11779935/
Abstract

The advancement of IT systems necessitates efficient communication methods essential across various sectors, from streaming platforms to cloud-based solutions and Industry 4.0 applications. Enhancing Quality of Service (QoS) in computer networks by focusing on bandwidth and communication delay is critical. Mechanisms like Active Queue Management (AQM) techniques are used, but more advanced solutions are needed to utilize the technological advances in communication technologies. Such advancements are Software-Defined Networks (SDN). Introduced by the SDN decoupling between the control plane and the data plane, it enables advanced real-time traffic shaping and centralized traffic management. This shift allows dynamic routing and improved QoS mechanisms, with research exploring multi-path routing. This paper proposes an extension to the Fluid Flow analysis model for complex networks. This modification allows for the simulation of various networking topologies and can be used to test novel routing and active queue management algorithms in more detail. The obtained numerical analysis demonstrates the model's advantages over traditional methods, enabling the exploration of new scenarios.

摘要

信息技术系统的发展需要高效的通信方法，这在各个领域都至关重要，从流媒体平台到基于云的解决方案以及工业4.0应用。通过关注带宽和通信延迟来提高计算机网络的服务质量（QoS）至关重要。诸如主动队列管理（AQM）技术之类的机制被使用，但需要更先进的解决方案来利用通信技术的技术进步。这种进步就是软件定义网络（SDN）。由控制平面和数据平面之间的SDN解耦引入，它实现了先进的实时流量整形和集中式流量管理。这种转变允许动态路由和改进的QoS机制，同时有研究探索多路径路由。本文提出了一种针对复杂网络的流体流动分析模型的扩展。这种修改允许模拟各种网络拓扑，并且可用于更详细地测试新颖的路由和主动队列管理算法。所获得的数值分析证明了该模型相对于传统方法的优势，能够探索新的场景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/2f98032860e0/41598_2025_88162_Fig19_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/9c4da0421f23/41598_2025_88162_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/7ff68255aef2/41598_2025_88162_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/abeb3bf4f390/41598_2025_88162_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/c6f0205b870d/41598_2025_88162_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/046fe0dc8734/41598_2025_88162_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/212776bd6701/41598_2025_88162_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/a4c740d8c0cb/41598_2025_88162_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/608ea35443c8/41598_2025_88162_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/82e13899af47/41598_2025_88162_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/de078dcf137c/41598_2025_88162_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/675431453830/41598_2025_88162_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/084840e175ad/41598_2025_88162_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/7be2f0b53d04/41598_2025_88162_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/675d19170d88/41598_2025_88162_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/dc0b97e8e5bf/41598_2025_88162_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/4b88cbf9ccf8/41598_2025_88162_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/5f095a319eca/41598_2025_88162_Fig17_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/351e7ec62cd2/41598_2025_88162_Fig18_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/2f98032860e0/41598_2025_88162_Fig19_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/9c4da0421f23/41598_2025_88162_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/7ff68255aef2/41598_2025_88162_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/abeb3bf4f390/41598_2025_88162_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/c6f0205b870d/41598_2025_88162_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/046fe0dc8734/41598_2025_88162_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/212776bd6701/41598_2025_88162_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/a4c740d8c0cb/41598_2025_88162_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/608ea35443c8/41598_2025_88162_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/82e13899af47/41598_2025_88162_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/de078dcf137c/41598_2025_88162_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/675431453830/41598_2025_88162_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/084840e175ad/41598_2025_88162_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/7be2f0b53d04/41598_2025_88162_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/675d19170d88/41598_2025_88162_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/dc0b97e8e5bf/41598_2025_88162_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/4b88cbf9ccf8/41598_2025_88162_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/5f095a319eca/41598_2025_88162_Fig17_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/351e7ec62cd2/41598_2025_88162_Fig18_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e4/11779935/2f98032860e0/41598_2025_88162_Fig19_HTML.jpg

相似文献

1
A fluid flow model for the software defined wide area networks analysis.一种用于软件定义广域网分析的流体流动模型。
Sci Rep. 2025 Jan 29;15(1):3713. doi: 10.1038/s41598-025-88162-6.
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
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.
4
Efficient traffic management with adaptive SDN in vehicular networks.车载网络中基于自适应软件定义网络的高效流量管理
Sci Rep. 2025 Apr 6;15(1):11785. doi: 10.1038/s41598-025-96365-0.
5
Application of SDN Network Traffic Prediction Based on Speech Recognition in Educational Information Optimization Platform.基于语音识别的 SDN 网络流量预测在教育信息优化平台中的应用。
Comput Intell Neurosci. 2022 Jul 22;2022:5716698. doi: 10.1155/2022/5716698. eCollection 2022.
6
On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks.面向软件定义车联网的高效分层架构设计。
Sensors (Basel). 2021 Feb 17;21(4):1400. doi: 10.3390/s21041400.
7
Termite inspired algorithm for traffic engineering in hybrid software defined networks.混合软件定义网络中用于流量工程的白蚁启发式算法
PeerJ Comput Sci. 2020 Aug 17;6:e283. doi: 10.7717/peerj-cs.283. eCollection 2020.
8
Augmenting Speech Quality Estimation in Software-Defined Networking Using Machine Learning Algorithms.使用机器学习算法增强软件定义网络中的语音质量估计。
Sensors (Basel). 2021 May 17;21(10):3477. doi: 10.3390/s21103477.
9
Enhancing QoS of Telecom Networks through Server Load Management in Software-Defined Networking (SDN).通过软件定义网络（SDN）中的服务器负载管理提升电信网络的服务质量（QoS）。
Sensors (Basel). 2023 Nov 22;23(23):9324. doi: 10.3390/s23239324.
10
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.

本文引用的文献

1
On the Transient Queue with the Dropping Function.关于具有丢弃功能的瞬态队列
Entropy (Basel). 2020 Jul 28;22(8):825. doi: 10.3390/e22080825.