.png){kind=icon}
返回
Modeling Residual-Geometric Flow Sampling
.png){kind=icon}
OA
AI
DOI：10.1109/TNET.2012.2231435.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Traffic monitoring and estimation of flow parameters in high-speed routers have recently become challenging as the Internet grew in both scale and complexity. In this paper, we focus on a family of flow-size estimation algorithms we call Residual-Geometric Sampling (RGS), which generates a random point within each flow according to a geometric random variable and records all remaining packets in a flow counter. Our analytical investigation shows that previous estimation algorithms based on this method exhibit bias in recovering flow statistics from the sampled measurements. To address this problem, we derive a novel set of unbiased estimators for RGS, validate them using real Internet traces, and show that they provide an accurate and scalable solution to Internet traffic monitoring.
Keyword:
Flow-size estimation
traffic sampling
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
3.6
论文数:
4.4K
被引数:
9.5K
机构
引用论文
.png){kind=icon}
查看更多内容