Abstract
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.
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