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Diamond Sketch: Accurate Per-flow Measurement for Big Streaming Data

机译：Diamond Sketch：大流量数据的精确每流测量

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摘要

Per-flow measurement is a critical issue in computer networks, and one of its key tasks is to count the number of packets in each flow (for big streaming data). The literature has demonstrated that sketch is the most memory-efficient data structure for the counting task, and is widely used in distributed systems. Existing sketches often use many counters that are of the same size to record the number of packets in a flow, thus the counters are forced to be large enough to accommodate the size of the largest flow. Unfortunately, as most flows are small (i.e., mice flows) and only a very few flows are large (i.e., elephant flows), many counters represent very small values, which is a waste of memory. Sketches are often stored in fast but expensive memory (e.g., SRAM), thus it is critical to achieve high memory efficiency. To address this issue, we propose a novel sketch, namely the Diamond sketch. The Diamond sketch is composed of atom sketches, and each atom sketch uses small counters. The key idea of Diamond is to dynamically assign an appropriate number of atom sketches to each flow on demand, thus optimizing memory efficiency. Experimental results show that the Diamond sketch outperforms the best of the five typical sketches by up to 508.3 times in terms of relative error while keeping comparable speed. We made the source code of all the six sketches available on GitHub [1] .
机译：每流测量是计算机网络中的一个关键问题，它的关键任务之一是计算每个流中的数据包数量（对于大型流数据）。文献已经证明，草图是用于计数任务的内存效率最高的数据结构，并且已广泛用于分布式系统中。现有的草图通常使用许多大小相同的计数器来记录流中的数据包数量，因此迫使计数器足够大以容纳最大流的大小。不幸的是，由于大多数流量很小（即，老鼠流量），而只有很少的流量很大（即，大象流量），所以许多计数器代表的值很小，这浪费了内存。草图通常存储在快速但昂贵的内存中（例如SRAM），因此实现高存储效率至关重要。为了解决这个问题，我们提出了一种新颖的草图，即钻石草图。 Diamond草图由原子草图组成，每个原子草图都使用小计数器。 Diamond的关键思想是根据需要为每个流动态分配适当数量的原子草图，从而优化内存效率。实验结果表明，Diamond草图在保持可比速度的同时，相对误差方面比五个典型草图中的最佳表现高出508.3倍。我们在GitHub [1]上提供了所有六个草图的源代码。

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