高速链路上的自适应聚合流量测量

Guang Cheng, J. Gong
{"title":"高速链路上的自适应聚合流量测量","authors":"Guang Cheng, J. Gong","doi":"10.1109/ICCS.2008.4737246","DOIUrl":null,"url":null,"abstract":"While network traffic may be characterized by many different criteria, it is ease to aggregate traffic along one dimension at a time. Unfortunately, by aggregating traffic along any single dimension, the network manager inevitably loses some interesting information. While the network manager can expose this structure by using finer grained representations, such as flows, he then must manage the excessive detail contained in such a representation. We define our traffic clusters in terms of the five fields typically used to define a fine-grained flow: source IP address, destination IP address, protocol, source port and destination port. Unlike others flow monitoring methods, such as NetFlow and ANF, we only keep the heavy-tailed flows and sampled short flows on a non-uniform sampling method with the flow length. The aggregation traffic can be estimated by these sampled flows and can keep the estimated accuracy at the same time. Experiment studies show our approach can significantly improve both the accuracy and efficiency in network aggregation flow monitoring comparing to other existing approaches.","PeriodicalId":208126,"journal":{"name":"2008 11th IEEE Singapore International Conference on Communication Systems","volume":"232 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Adaptive aggregation flow measurement on high speed links\",\"authors\":\"Guang Cheng, J. Gong\",\"doi\":\"10.1109/ICCS.2008.4737246\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"While network traffic may be characterized by many different criteria, it is ease to aggregate traffic along one dimension at a time. Unfortunately, by aggregating traffic along any single dimension, the network manager inevitably loses some interesting information. While the network manager can expose this structure by using finer grained representations, such as flows, he then must manage the excessive detail contained in such a representation. We define our traffic clusters in terms of the five fields typically used to define a fine-grained flow: source IP address, destination IP address, protocol, source port and destination port. Unlike others flow monitoring methods, such as NetFlow and ANF, we only keep the heavy-tailed flows and sampled short flows on a non-uniform sampling method with the flow length. The aggregation traffic can be estimated by these sampled flows and can keep the estimated accuracy at the same time. Experiment studies show our approach can significantly improve both the accuracy and efficiency in network aggregation flow monitoring comparing to other existing approaches.\",\"PeriodicalId\":208126,\"journal\":{\"name\":\"2008 11th IEEE Singapore International Conference on Communication Systems\",\"volume\":\"232 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 11th IEEE Singapore International Conference on Communication Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCS.2008.4737246\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 11th IEEE Singapore International Conference on Communication Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCS.2008.4737246","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

虽然网络流量可以用许多不同的标准来表征,但每次沿着一个维度聚合流量是很容易的。不幸的是,通过沿着任何单一维度聚合流量,网络管理器不可避免地会丢失一些有趣的信息。虽然网络管理员可以通过使用更细粒度的表示(如流)来公开此结构,但他随后必须管理这种表示中包含的过多细节。我们根据通常用于定义细粒度流的五个字段来定义流量集群:源IP地址、目的IP地址、协议、源端口和目的端口。与其他流量监测方法(如NetFlow和ANF)不同,我们只保留重尾流和采样短流的非均匀采样方法与流量长度。通过这些采样流可以估计聚合流量,同时可以保持估计的准确性。实验研究表明,与现有的网络聚合流量监测方法相比,该方法可以显著提高网络聚合流量监测的准确性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive aggregation flow measurement on high speed links
While network traffic may be characterized by many different criteria, it is ease to aggregate traffic along one dimension at a time. Unfortunately, by aggregating traffic along any single dimension, the network manager inevitably loses some interesting information. While the network manager can expose this structure by using finer grained representations, such as flows, he then must manage the excessive detail contained in such a representation. We define our traffic clusters in terms of the five fields typically used to define a fine-grained flow: source IP address, destination IP address, protocol, source port and destination port. Unlike others flow monitoring methods, such as NetFlow and ANF, we only keep the heavy-tailed flows and sampled short flows on a non-uniform sampling method with the flow length. The aggregation traffic can be estimated by these sampled flows and can keep the estimated accuracy at the same time. Experiment studies show our approach can significantly improve both the accuracy and efficiency in network aggregation flow monitoring comparing to other existing approaches.
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
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:604180095
Book学术官方微信