Dynamic Perfect Hashing: Upper and Lower Bounds
SIAM Journal on Computing
The Bloomier filter: an efficient data structure for static support lookup tables
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Segmented hash: an efficient hash table implementation for high performance networking subsystems
Proceedings of the 2005 ACM symposium on Architecture for networking and communications systems
Design of a novel statistics counter architecture with optimal space and time efficiency
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
Fast packet classification using bloom filters
Proceedings of the 2006 ACM/IEEE symposium on Architecture for networking and communications systems
Designing packet buffers for router linecards
IEEE/ACM Transactions on Networking (TON)
Less hashing, same performance: Building a better Bloom filter
Random Structures & Algorithms
BUFFALO: bloom filter forwarding architecture for large organizations
Proceedings of the 5th international conference on Emerging networking experiments and technologies
Fast and Scalable Pattern Matching for Network Intrusion Detection Systems
IEEE Journal on Selected Areas in Communications
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In this paper, we consider the problem of designing a data structure that can perform fast multiple-set membership testing in deterministic time. Our primary goal is to develop a hardware implementation of the data structure that uses only embedded memory blocks. Prior efforts to solve this problem involve hashing into multiple Bloom filters. Such approach needs a priori knowledge of the number of elements in each set in order to size the Bloom filter. We use a single-Bloom-filter-based approach and use multiple sets of hash functions to code for the set (group) id. Since a single Bloom filter is used, it does not need a priori knowledge of the distribution of the elements across the different sets. We show how to improve the performance of the data structure by using constant-weight error-correcting codes for coding the group id. Using error-correcting codes improves the performance of these data structures especially when there are a large number of sets. We also outline an efficient hardware-based approach to generate the large number of hash functions that we need for this data structure. The resulting data structure, COMB, is amenable to a variety of time-critical network applications.