计算机科学
试验台
散列函数
素描
过程(计算)
跟踪(心理语言学)
流量(数学)
内存管理
比例(比率)
计算机工程
并行计算
算法
计算机硬件
数学
计算机网络
程序设计语言
哲学
语言学
量子力学
半导体存储器
物理
几何学
作者
Jiawei Huang,Wenlu Zhang,Yijun Li,Lin Li,Zhaoyi Li,Ye Jin,Jianxin Wang
标识
DOI:10.1109/tnet.2022.3199506
摘要
Identifying heavy flows is essential for network management. However, it is challenging to detect heavy flow quickly and accurately under the highly dynamic traffic and rapid growth of network capacity. Existing heavy flow detection schemes can make a trade-off in efficiency, accuracy and speed. However, these schemes still require memory large enough to obtain acceptable performance. To address this issue, we propose ChainSketch, which has the advantages of good memory efficiency, high accuracy and fast detection. Specifically, ChainSketch uses the selective replacement strategy to mitigate the over-estimation issue. Meanwhile, ChainSketch utilizes the hash chain and compact structure to improve memory efficiency. We implement the ChainSketch on OVS platform, P4-based testbed and large-scale simulations to process heavy hitter and heavy changer detection. The results of trace-driven tests show that, ChainSketch greatly improves the F1-score by up to $3.43\times $ compared with the state-of-the-art solutions especially for small memory.
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