IBN@Cloud:基于意图的云和叠加网络协调系统

IF 2.9 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mir Muhammad Suleman Sarwar;Afaq Muhammad;Wang-Cheol Song
{"title":"IBN@Cloud:基于意图的云和叠加网络协调系统","authors":"Mir Muhammad Suleman Sarwar;Afaq Muhammad;Wang-Cheol Song","doi":"10.23919/JCN.2023.000051","DOIUrl":null,"url":null,"abstract":"This paper presents an intent-based networking (IBN) system for the orchestration of OpenStack-based clouds and overlay networks between multiple clouds. Clouds need to communicate with other clouds for various reasons such as reducing latency and overcoming single points of failure. An overlay network provides connectivity between multiple Clouds for communication. Moreover, there can be several paths of communication between a source and a destination cloud in the overlay network. A machine learning model can be used to proactively select the best path for efficient network performance. Communication between the source and destination can then be established over the selected path. Communication in such type of a scenario requires complex networking configurations. IBN provides a closed-loop and Intelligent system for cloud to cloud communication. To this end, IBN abstracts complex networking and cloud configurations by receiving an intent from a user, translating the intent, generating complex configurations for the intent, and deploying the configurations, thereby assuring the intent. Therefore, the IBN that is presented here has three major features: (1) It can deploy an OpenStack cloud at a target machine, (2) it can deploy GENEVE tunnels between different clouds that form an overlay network, and (3) it can then leverage the advantages of machine learning to find the best path for communication between any two clouds. As machine learning is an essential component of the intelligent IBN system, two linear and three non-linear models were tested. RNN, LSTM, and GRU models were employed for non-linear modeling. Linear regression and SVR models were employed for linear modeling. Overall all the non-linear models outperformed the linear model with an 81% R\n<sup>2</sup>\n score, exhibiting similar performance. Linear models also showed similar performance but with lower accuracy. The testbed contains an overlay network of 11 GENEVE tunnels between 7 OpenStack-based clouds deployed in Malaysia, Korea, Pakistan, and Cambodia at TEIN.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 1","pages":"131-146"},"PeriodicalIF":2.9000,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10416318","citationCount":"0","resultStr":"{\"title\":\"IBN@Cloud: An intent-based cloud and overlay network orchestration system\",\"authors\":\"Mir Muhammad Suleman Sarwar;Afaq Muhammad;Wang-Cheol Song\",\"doi\":\"10.23919/JCN.2023.000051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an intent-based networking (IBN) system for the orchestration of OpenStack-based clouds and overlay networks between multiple clouds. Clouds need to communicate with other clouds for various reasons such as reducing latency and overcoming single points of failure. An overlay network provides connectivity between multiple Clouds for communication. Moreover, there can be several paths of communication between a source and a destination cloud in the overlay network. A machine learning model can be used to proactively select the best path for efficient network performance. Communication between the source and destination can then be established over the selected path. Communication in such type of a scenario requires complex networking configurations. IBN provides a closed-loop and Intelligent system for cloud to cloud communication. To this end, IBN abstracts complex networking and cloud configurations by receiving an intent from a user, translating the intent, generating complex configurations for the intent, and deploying the configurations, thereby assuring the intent. Therefore, the IBN that is presented here has three major features: (1) It can deploy an OpenStack cloud at a target machine, (2) it can deploy GENEVE tunnels between different clouds that form an overlay network, and (3) it can then leverage the advantages of machine learning to find the best path for communication between any two clouds. As machine learning is an essential component of the intelligent IBN system, two linear and three non-linear models were tested. RNN, LSTM, and GRU models were employed for non-linear modeling. Linear regression and SVR models were employed for linear modeling. Overall all the non-linear models outperformed the linear model with an 81% R\\n<sup>2</sup>\\n score, exhibiting similar performance. Linear models also showed similar performance but with lower accuracy. The testbed contains an overlay network of 11 GENEVE tunnels between 7 OpenStack-based clouds deployed in Malaysia, Korea, Pakistan, and Cambodia at TEIN.\",\"PeriodicalId\":54864,\"journal\":{\"name\":\"Journal of Communications and Networks\",\"volume\":\"26 1\",\"pages\":\"131-146\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-01-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10416318\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Communications and Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10416318/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Communications and Networks","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10416318/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

本文介绍了一种基于意图的网络(IBN)系统,用于协调基于 OpenStack 的云和多个云之间的覆盖网络。出于减少延迟和克服单点故障等各种原因,云需要与其他云进行通信。覆盖网络为多个云之间的通信提供连接。此外,在叠加网络中,源云和目标云之间可以有多个通信路径。机器学习模型可用于主动选择最佳路径,以提高网络性能。然后就可以通过选定的路径建立源和目的地之间的通信。此类场景中的通信需要复杂的网络配置。IBN 为云到云通信提供了一个闭环智能系统。为此,IBN 通过接收用户的意图、翻译意图、为意图生成复杂的配置并部署配置,从而抽象出复杂的网络和云配置,从而确保意图的实现。因此,本文介绍的 IBN 有三大特点:(1) 它可以在目标机器上部署 OpenStack 云;(2) 它可以在不同云之间部署 GENEVE 隧道,从而形成一个覆盖网络;(3) 然后,它可以利用机器学习的优势,为任意两个云之间的通信找到最佳路径。由于机器学习是智能 IBN 系统的重要组成部分,因此测试了两种线性模型和三种非线性模型。非线性模型采用了 RNN、LSTM 和 GRU 模型。线性建模采用了线性回归和 SVR 模型。总体而言,所有非线性模型都优于线性模型,R2 得分为 81%,表现出相似的性能。线性模型也表现出相似的性能,但准确率较低。测试平台包含一个由 11 个 GENEVE 隧道组成的覆盖网络,这些隧道连接着 TEIN 在马来西亚、韩国、巴基斯坦和柬埔寨部署的 7 个基于 OpenStack 的云。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IBN@Cloud: An intent-based cloud and overlay network orchestration system
This paper presents an intent-based networking (IBN) system for the orchestration of OpenStack-based clouds and overlay networks between multiple clouds. Clouds need to communicate with other clouds for various reasons such as reducing latency and overcoming single points of failure. An overlay network provides connectivity between multiple Clouds for communication. Moreover, there can be several paths of communication between a source and a destination cloud in the overlay network. A machine learning model can be used to proactively select the best path for efficient network performance. Communication between the source and destination can then be established over the selected path. Communication in such type of a scenario requires complex networking configurations. IBN provides a closed-loop and Intelligent system for cloud to cloud communication. To this end, IBN abstracts complex networking and cloud configurations by receiving an intent from a user, translating the intent, generating complex configurations for the intent, and deploying the configurations, thereby assuring the intent. Therefore, the IBN that is presented here has three major features: (1) It can deploy an OpenStack cloud at a target machine, (2) it can deploy GENEVE tunnels between different clouds that form an overlay network, and (3) it can then leverage the advantages of machine learning to find the best path for communication between any two clouds. As machine learning is an essential component of the intelligent IBN system, two linear and three non-linear models were tested. RNN, LSTM, and GRU models were employed for non-linear modeling. Linear regression and SVR models were employed for linear modeling. Overall all the non-linear models outperformed the linear model with an 81% R 2 score, exhibiting similar performance. Linear models also showed similar performance but with lower accuracy. The testbed contains an overlay network of 11 GENEVE tunnels between 7 OpenStack-based clouds deployed in Malaysia, Korea, Pakistan, and Cambodia at TEIN.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.60
自引率
5.60%
发文量
66
审稿时长
14.4 months
期刊介绍: The JOURNAL OF COMMUNICATIONS AND NETWORKS is published six times per year, and is committed to publishing high-quality papers that advance the state-of-the-art and practical applications of communications and information networks. Theoretical research contributions presenting new techniques, concepts, or analyses, applied contributions reporting on experiences and experiments, and tutorial expositions of permanent reference value are welcome. The subjects covered by this journal include all topics in communication theory and techniques, communication systems, and information networks. COMMUNICATION THEORY AND SYSTEMS WIRELESS COMMUNICATIONS NETWORKS AND SERVICES.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信