Optimizing hash function number for BF-Based object locating algorithm

Authors:
Zhu Wang;Tiejian Luo
Affiliations:
Key Laboratory of Computational Geodynamics, CAS, China,Information Dynamic and Engineering Applications Laboratory, Graduate University of Chinese Academy of Sciences, GUCAS, Beijing, China;Key Laboratory of Computational Geodynamics, CAS, China,Information Dynamic and Engineering Applications Laboratory, Graduate University of Chinese Academy of Sciences, GUCAS, Beijing, China
Venue:
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
Year:
2012

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Abstract

High performance object locating is the hard undertaking in a distributed system. The quality of this work can be assessed by the response time, space utilization and hit rate which are the essential requirements for large-scale Internet applications. Bloom Filter (BF) is made of a number of hash functions which is the critical part of the object locating algorithm. But how many hash functions in BF are the best remains unsolved. This paper presents a method for estimating those numbers in BF's hash function configuration. Our theoretical analysis for figuring out the optimal hash number is given. That number has been crucial to construct a better BF-based algorithm. In order to verify the correctness of our theoretical result, we establish a simulation environment with 50 million objects which are scattered on one hundred nodes. The experiment for comparing traditional hash function number with our number is given. The experimental result shows that the BF with our optimized parameter can reduce the object locating time by 81- 91 percent. Furthermore, we demonstrate this method can be used in similar content randomly-located distributed systems.