Dynamic Reordering Bloom Filter

Abstract

In order to check a membership in multiple sets of bloom filter in a dynamic bloom filter, a sequential search is usually used. Since the distribution of queried data is unpredictable because the distribution has a feature of temporal locality. Therefore more search cost is incurred if queried data is stored in the peer which is corresponded to the Bloom Filter has lower query priority. In this paper, we introduce Dynamic Reordering Bloom Filter that can save the cost of searching Bloom Filter by dynamically reorder the searching sequence of multiple bloom filters in a dynamic bloom filter with One Memory Access Bloom Filter (OMABF) and checked in the order saved in Query Index (QI). The performance of the system is evaluated by Markov Chain. Simulation results show that our scheme on average has 43% better in searching performance comparing with the sequential methods, which is verified via three different trace log files.