Identifying Elephant Flows Using a Reversible MultiLayer Hashed Counting Bloom Filter

Identifying elephant flows is very important for many applications, such as differentiated services, load balancing and network management. Existing work requests relatively high burden. In this paper, we propose a new method to identify elephant flows. The proposed idea is based on a novel data structure called Reversible MultiLayer Hashed Counting Bloom Filter(RML-HCBF). An RML-HCBF includes a few of hash functions which select some consecutive bits from the original string as its function values. Although RML-HCBF does not preserve any flow identifier (ID) explicitly, the flow ID of an elephant can be reconstructed by using the overlapping of the hash bit strings. RML-HCBF can identify elephant flows without storing flow ID and performing flow ID lookup. We evaluate the performance of RML-HCBF through theoretical analysis and experiments on real network traffic traces. The results show that RML-HCBF can identify elephant flows accurately and efficiently.