Biblio

Hash-based data structures and algorithms are currently flourishing on the Internet. It is an effective way to store large amounts of information, especially for applications related to measurement, monitoring and security. At present, there are many hash table algorithms such as: Cuckoo Hash, Peacock Hash, Double Hash, Link Hash and D-left Hash algorithm. However, there are still some problems in these hash table algorithms, such as excessive memory space, long insertion and query operations, and insertion failures caused by infinite loops that require rehashing. This paper improves the kick-out mechanism of the Cuckoo Hash algorithm, and proposes a new hash table structure- Odd-Even Hash (OE Hash) algorithm. The experimental results show that OE Hash algorithm is more efficient than the existing Link Hash algorithm, Linear Hash algorithm, Cuckoo Hash algorithm, etc. OE Hash algorithm takes into account the performance of both query time and insertion time while occupying the least space, and there is no insertion failure that leads to rehashing, which is suitable for massive data storage.

Bloom filters (BFs) are fast and space-efficient data structures used for set membership queries in many applications. BFs are required to satisfy three key requirements: low space cost, high-speed lookups, and fast updates. Prior works do not satisfy these requirements at the same time. The standard BF does not support deletions of items and the variants that support deletions need additional space or performance overhead. The state-of-the-art cuckoo filters (CF) has high performance with seemingly low space cost. However, the CF suffers a critical issue of varying space cost per item. This is because the exclusive-OR (XOR) operation used by the CF requires the total number of buckets to be a power of two, leading to the space inflation. To address the issue, in this paper we propose a scalable variant of the cuckoo filter called additive and subtractive cuckoo filter (ASCF). We aim to improve the space efficiency while sustaining comparably high performance. The ASCF uses the addition and subtraction (ADD/SUB) operations instead of the XOR operation to compute an item's two candidate bucket indexes based on its fingerprint. Experimental results show that the ASCF achieves both low space cost and high performance. Compared to the CF, the ASCF reduces up to 1.9x space cost per item while maintaining the same lookup and update throughput. In addition, the ASCF outperforms other filters in both space cost and performance.