Empirical Evaluation of Traffic Shaping Algorithms for Time Sensitive Networking

Kuni Naik, D. Kumari, M. Tahiliani
{"title":"Empirical Evaluation of Traffic Shaping Algorithms for Time Sensitive Networking","authors":"Kuni Naik, D. Kumari, M. Tahiliani","doi":"10.1109/IBSSC56953.2022.10037572","DOIUrl":null,"url":null,"abstract":"Standard Ethernet networks cannot provide solutions to handle latency sensitive applications efficiently. The packet scheduling algorithms like First In First Out (FIFO), Class Based Queueing (CBQ), and others do not provide efficient solutions to Quality of Service (QoS) parameters like end-toend delay, packet loss, and jitter. Time Sensitive Networking (TSN) can be used as a solution to provide QoS to time sensitive applications. TSN has emerged as a future of realtime communication. The main advantage of TSN is that it enables determinism by supporting time critical traffic while the best effort traffic is also present in the network. This paper explores two of the most popular and widespread traffic shaping mechanisms in TSN: Time Aware Shaper (TAS) and Credit Based Shaper (CBS). IEEE 802.1Qbv is used for delivering time assurance using TAS. CBS is a key traffic shaping algorithm to provide bandwidth assurance to the time critical and real time traffic, such as the audio traffic. This paper evaluates TAS and CBS using TSN enabled Network Interface Cards (NIC) with time synchronization, real time kernel and real traffic, which includes time sensitive traffic and elastic background traffic.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Bombay Section Signature Conference (IBSSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IBSSC56953.2022.10037572","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Standard Ethernet networks cannot provide solutions to handle latency sensitive applications efficiently. The packet scheduling algorithms like First In First Out (FIFO), Class Based Queueing (CBQ), and others do not provide efficient solutions to Quality of Service (QoS) parameters like end-toend delay, packet loss, and jitter. Time Sensitive Networking (TSN) can be used as a solution to provide QoS to time sensitive applications. TSN has emerged as a future of realtime communication. The main advantage of TSN is that it enables determinism by supporting time critical traffic while the best effort traffic is also present in the network. This paper explores two of the most popular and widespread traffic shaping mechanisms in TSN: Time Aware Shaper (TAS) and Credit Based Shaper (CBS). IEEE 802.1Qbv is used for delivering time assurance using TAS. CBS is a key traffic shaping algorithm to provide bandwidth assurance to the time critical and real time traffic, such as the audio traffic. This paper evaluates TAS and CBS using TSN enabled Network Interface Cards (NIC) with time synchronization, real time kernel and real traffic, which includes time sensitive traffic and elastic background traffic.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
时间敏感网络流量整形算法的实证评价
标准以太网不能提供有效处理延迟敏感应用程序的解决方案。诸如先进先出(FIFO)、基于类的队列(CBQ)等数据包调度算法不能有效地解决服务质量(QoS)参数,如端到端延迟、数据包丢失和抖动。TSN (Time Sensitive Networking)是一种为时间敏感型应用提供QoS的解决方案。TSN已经成为实时通信的未来。TSN的主要优点是,它通过支持时间关键型流量来实现确定性,同时网络中也存在尽力而为的流量。本文探讨了TSN中最流行和最广泛的两种流量整形机制:时间感知整形器(TAS)和基于信用的整形器(CBS)。IEEE 802.1Qbv用于使用TAS提供时间保证。CBS是一种关键的流量整形算法,可以为音频等时间关键型实时流量提供带宽保障。本文利用具有时间同步、实时内核和真实流量(包括时间敏感流量和弹性后台流量)的支持TSN的网卡对TAS和CBS进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Decentralized Ride Hailing System using Blockchain and IPFS Implementation of RFID-based Lab Inventory System Monkeypox Skin Lesion Classification Using Transfer Learning Approach A Solution to the Techno-Economic Generation Expansion Planning using Enhanced Dwarf Mongoose Optimization Algorithm Citation Count Prediction Using Different Time Series Analysis Models
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1