Virtual Filter for Non-duplicate Sampling

Chaoyi Ma, Haibo Wang, Olufemi O. Odegbile, Shigang Chen
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引用次数: 2

Abstract

Sampling is key to handling mismatch between the line rate and the throughput of a network traffic measurement module. Flow-spread measurement requires non-duplicate sampling, which only samples the elements (carried in packet header or payload) in each flow when they appear for the first time and blocks them for subsequent appearances. The only prior work for non-duplicate sampling incurs considerable overhead, and has two practical limitations: It lacks a mechanism to set an appropriate sampling probability under dynamic traffic conditions, and it cannot efficiently handle multiple concurrent sampling tasks. This paper proposes a virtual filter design for non-duplicate sampling, which reduces the processing overhead by about half and reduces the memory overhead by an order of magnitude or more under some practical settings. It has a mechanism to automatically adapt its sampling probability to the traffic dynamics. It can be extended to solve a new problem called non-duplicate distribution sampling, which samples packets based on a probability distribution to support multiple concurrent measurement tasks.
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非重复采样的虚拟过滤器
采样是处理网络流量测量模块的线路速率和吞吐量不匹配的关键。流扩展测量需要非重复采样,它只在每个流第一次出现时对元素(包头或负载中携带的元素)进行采样,并在后续出现时阻止它们。非重复采样的唯一先验工作带来了相当大的开销,并且存在两个实际限制:缺乏在动态流量条件下设置适当采样概率的机制,并且不能有效地处理多个并发采样任务。本文提出了一种用于非重复采样的虚拟滤波器设计,在一些实际设置下,该设计将处理开销减少了大约一半,并将内存开销减少了一个数量级或更多。它具有一种自动调整采样概率以适应交通动态的机制。它可以扩展到解决一个新的问题,称为非重复分布采样,即基于概率分布对数据包进行采样,以支持多个并发测量任务。
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