Evaluation and Design of Non-cryptographic Hash Functions for Network Data Stream Algorithms

Non-cryptographic hash function is the core algorithm in network data stream technologies, its performance plays a crucial role in data stream algorithms. In this paper, two new quality criteria active flow metric and homology hash value correlation metric are firstly proposed for evaluating hash functions used in data stream algorithms. Experiments towards the metrics defined on 15 representative hash functions are performed using the real IPv6 network data captured from CERNET backbone. Bitwise operators are common candidates for implementing hash functions. We experimentally prove that XOR can introduce the most entropy to hash values compared with other 3 operators. On the basis of operator analysis, we design a novel hash function utilizing Genetic Programming for data stream algorithm and network measurement. It can compete with the state of the art hash functions.