使用稀疏图计数器的详细网络测量：理论
Detailed Network Measurements Using Sparse Graph Counters: The Theory
摘要：
测量网络流量大小对于计费/计费、网络取证和安全等任务非常重要。按流计费被认为是困难的，因为它需要以非常高的速度更新许多计数器;然而，存储计数器所需的大型快速存储器非常昂贵。因此，目前的方法旨在获得近似的流量计数，也就是说，检测大型大象流量，然后测量它们的大小。最近，作者和他们的合作者开发了[1]一种新的方法，用于每个流的流量测量，该方法快速，高效且准确。这种方法的核心是一种新的计数器架构，称为“计数器辫子”。在本文中，我们分析了最大似然（ML）流大小估计算法下的计数器编织架构的性能，并表明它是最优的;也就是说，存储流大小所需的位数与熵下限相匹配。虽然ML算法是最佳的，但它太复杂而无法实现。在[1]中，我们开发了一种易于实现且有效的消息传递算法来估计流大小。
英文摘要：
Measuring network flow sizes is important for tasks like accounting/billing, network forensics and security. Per-flow accounting is considered hard because it requires that many counters be updated at a very high speed; however, the large fast memories needed for storing the counters are prohibitively expensive. Therefore, current approaches aim to obtain approximate flow counts; that is, to detect large elephant flows and then measure their sizes. Recently the authors and their collaborators have developed [1] a novel method for per-flow traffic measurement that is fast, highly memory efficient and accurate. At the core of this method is a novel counter architecture called "counter braids.'' In this paper, we analyze the performance of the counter braid architecture under a Maximum Likelihood (ML) flow size estimation algorithm and show that it is optimal; that is, the number of bits needed to store the size of a flow matches the entropy lower bound. While the ML algorithm is optimal, it is too complex to implement. In [1] we have developed an easy-to-implement and efficient message passing algorithm for estimating flow sizes.
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