Identifying elephant flows by building a Reversible Counting Bloom Filter
Weijiang Liu, Dandan Huo, Hongbo Liu · 2009
Identifying elephant flows is very important in developing effective and efficient traffic engineering schemes. In addition, obtaining the statistics of these flows is also very useful for network operation and management. In this paper, we propose a novel method to identify elephant flows by introducing a Reversible Counting Bloom Filter (RCBF), a special Counting Bloom Filter. The RCBF consists of 6 Hash functions which projectively select some consecutive bits from original strings as function values. We obtain the information of elephant flow using the overlapping of hash bit strings of the RCBF and heavy-tailed feature of flow distribution. The theoretical analysis and experiment results show that this method can precisely and efficiently identify elephant flows.