RePizer: a framework for prioritization of software requirements#

Yun-xiang ZHAO; Wan-xin ZHANG; Dong-sheng LI; Zhen HUANG; Min-ne LI; Xi-cheng LU

doi:10.1631/FITEE.1500487

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RePizer: a framework for prioritization of software requirements#

Regular Papers | Updated：2022-05-19

- RePizer: a framework for prioritization of software requirements#
- 负载均衡的分布式指纹识别系统
- Frontiers of Information Technology & Electronic Engineering Vol. 17, Issue 8, Pages: 766-780(2016)
- Affiliations：
  
  National Laboratory for Parallel and Distributed Processing, College of Computer, National University of Defense Technology, Changsha 410003, China
- Author bio：
  
  E-mail:zhaoyx1993@163.com
  E-mail:camu7s@163.com
  E-mail:dsli@nudt.edu.cn
  E-mail:maths_www@163.com
  E-mail:litoeknee@gmail.com
- Funds：
- DOI：10.1631/FITEE.1500487
  CLC： TP316.4
- Received：29 December 2015，
  
  Accepted：12 April 2016，
  
  Published：2016-08
- Accepted：
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Yun-xiang ZHAO, Wan-xin ZHANG, Dong-sheng LI, et al. RePizer: a framework for prioritization of software requirements#[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(8): 766-780.
DOI：

Yun-xiang ZHAO, Wan-xin ZHANG, Dong-sheng LI, et al. RePizer: a framework for prioritization of software requirements#[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(8): 766-780. DOI： 10.1631/FITEE.1500487.

摘要

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%。

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.

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references

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