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CLC number: TP316.4

On-line Access: 2016-08-05

Received: 2015-12-29

Revision Accepted: 2016-04-12

Crosschecked: 2016-07-11

Cited: 0

Clicked: 3705

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Dong-sheng LI

http://orcid.org/0000-0001-9743-2034

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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.8 P.766-780

http://doi.org/10.1631/FITEE.1500487


Pegasus: a distributed and load-balancing fingerprint identification system


Author(s):  Yun-xiang Zhao, Wan-xin Zhang, Dong-sheng LI, Zhen Huang, Min-ne Li, Xi-cheng Lu

Affiliation(s):  National Laboratory for Parallel and Distributed Processing, College of Computer, National University of Defense Technology, Changsha 410003, China

Corresponding email(s):   zhaoyx1993@163.com, camu7s@163.com, dsli@nudt.edu.cn, maths_www@163.com, litoeknee@gmail.com

Key Words:  Distributed fingerprint identification, Distributed MongoDB, Load balancing


Yun-xiang Zhao, Wan-xin Zhang, Dong-sheng LI, Zhen Huang, Min-ne Li, Xi-cheng Lu. Pegasus: a distributed and load-balancing fingerprint identification system[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(8): 766-780.

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author="Yun-xiang Zhao, Wan-xin Zhang, Dong-sheng LI, Zhen Huang, Min-ne Li, Xi-cheng Lu",
journal="Frontiers of Information Technology & Electronic Engineering",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500487"
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Abstract: 
Fingerprint has been widely used in a variety of biometric identification systems in the past several years due to its uniqueness and immutability. With the rapid development of fingerprint identification techniques, many fingerprint identification systems are in urgent need to deal with large-scale fingerprint storage and high concurrent recognition queries, which bring huge challenges to the system. In this circumstance, we design and implement a distributed and load-balancing fingerprint identification system named Pegasus, which includes a distributed feature extraction subsystem and a distributed feature storage subsystem. The feature extraction procedure combines the Hadoop Image Processing Interface (HIPI) library to enhance its overall processing speed; the feature storage subsystem optimizes MongoDB’s default load balance strategy to improve the efficiency and robustness of Pegasus. Experiments and simulations are carried out, and results show that Pegasus can reduce the time cost by 70% during the feature extraction procedure. Pegasus also balances the difference of access load among front-end mongos nodes to less than 5%. Additionally, Pegasus reduces over 40% of data migration among back-end data shards to obtain a more reasonable data distribution based on the operation load (insertion, deletion, update, and query) of each shard.

The authors present a distributed load balanced fingerprint identification system using big data technologies. The paper is interesting and novel.

负载均衡的分布式指纹识别系统

概要:指纹的唯一性和不变性使得它在各类生物识别系统中得到了广泛的应用。随着指纹识别技术的发展,大规模的指纹存储和系统高并发的需求给指纹识别系统带来了新的挑战。面对这种挑战,我们设计并实现了一个负载均衡的分布式指纹识别系统,它包括分布式指纹特征提取子系统和分布式指纹特征存储子系统两部分。在指纹特征提取的过程中,特征提取与Hadoop图片处理接口(HIPI)的结合使得特征提取的效率得到大幅度提升;特征存储子系统对MongoDB默认的负载均衡策略进行了优化,使得鲁棒性得到了明显提高。相关的试验和模拟表明,相比于Hadoop默认的小文件处理机制,我们的系统可以在指纹特征提取的过程中减少约70%的处理时间;优化后的MongoDB负载均衡策略可以将分布式MongoDB系统的前端mongos负载差距控制在5%以下,基于操作负载(增、删、改、查)的后端数据存储负载均衡策略将由数据迁移带来的时间开销降低了约40%。

关键词:分布式指纹识别系统;分布式MongoDB;负载均衡

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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