In Band Network Telemetry Overhead Reduction Based on Data Flows Sampling and Recovering

S. Sardellitti, Marco Polverini, S. Barbarossa, A. Cianfrani, P. Lorenzo, M. Listanti
{"title":"In Band Network Telemetry Overhead Reduction Based on Data Flows Sampling and Recovering","authors":"S. Sardellitti, Marco Polverini, S. Barbarossa, A. Cianfrani, P. Lorenzo, M. Listanti","doi":"10.1109/NetSoft57336.2023.10175471","DOIUrl":null,"url":null,"abstract":"In band Network Telemetry (INT) is a technique aiming at collecting telemetry information by inserting it inside the data packets, instead of relying on classical centralized monitoring elements that periodically query the network devices. The main drawback of INT is represented by the introduced per-packet overhead, that could negatively affect some traffic flows, especially those having stringent QoS requirements. To deal with the increase in the packet length caused by INT, in this paper we introduce the Sampling and Recovering paradigm to overcome the classical Collect Everything approach where all the INT data must be gathered. The proposed approach hinges on signal processing strategies to sample and recover sparse flow signals. The key idea is to reduce the number of INT data to collect and exploit signal reconstruction algorithms to obtain the unseen samples. The preliminary performance evaluation shows that the 18% of INT data are enough to get an accurate reconstruction of the overall network situation, while allowing for 90% of overhead reduction with respect to the Collect Everything case.","PeriodicalId":223208,"journal":{"name":"2023 IEEE 9th International Conference on Network Softwarization (NetSoft)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 9th International Conference on Network Softwarization (NetSoft)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NetSoft57336.2023.10175471","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

In band Network Telemetry (INT) is a technique aiming at collecting telemetry information by inserting it inside the data packets, instead of relying on classical centralized monitoring elements that periodically query the network devices. The main drawback of INT is represented by the introduced per-packet overhead, that could negatively affect some traffic flows, especially those having stringent QoS requirements. To deal with the increase in the packet length caused by INT, in this paper we introduce the Sampling and Recovering paradigm to overcome the classical Collect Everything approach where all the INT data must be gathered. The proposed approach hinges on signal processing strategies to sample and recover sparse flow signals. The key idea is to reduce the number of INT data to collect and exploit signal reconstruction algorithms to obtain the unseen samples. The preliminary performance evaluation shows that the 18% of INT data are enough to get an accurate reconstruction of the overall network situation, while allowing for 90% of overhead reduction with respect to the Collect Everything case.
基于数据流采样与恢复的带内网络遥测开销降低
带内网络遥测(INT)是一种旨在通过将遥测信息插入数据包中来收集遥测信息的技术,而不是依赖于传统的定期查询网络设备的集中式监控元素。INT的主要缺点是引入的每个数据包开销,这可能会对某些流量产生负面影响,特别是那些具有严格QoS要求的流量。为了处理由INT引起的数据包长度的增加,在本文中我们引入了采样和恢复范式,以克服必须收集所有INT数据的经典Collect Everything方法。该方法依赖于信号处理策略来采样和恢复稀疏流信号。其关键思想是减少需要收集的INT数据的数量,并利用信号重构算法来获取看不见的样本。初步的性能评估表明,18%的INT数据足以获得整个网络情况的准确重建,同时与Collect Everything情况相比,可以减少90%的开销。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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