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 memori
es 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.
