Research of the Bloom Filters Adaptive Mechanism in Skewed Traffic
To overcome the shortcoming the counters of Count Bloom Filters are easy to be overflow when CBF counts the multiplicity of items in many emerging applications data streams with skewed distributions,this paper proposes an Adaptive Count Bloom Filter(ABF),which uses a scaleable logical counter instead of the fixed size physical counter and the logical counter consists of some physical counters.In the initial stage the logical counter is similar to the physical counter,but while the counter becomes overflow the logical counter will override the external counter to increase its capability to assure the right value.The experimental results show that ABF adapts to the change of multiplicity of items well and the probability of error of ABF does not get a remarkable worse,so ABF outperforms most other Count Bloom Filters on counting multiplicity of items in skewed distributions traffic.