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基于GemFire的海量数据计算性能实验分析
引用本文:徐翔,邹复民,廖律超,朱铨.基于GemFire的海量数据计算性能实验分析[J].计算机应用,2013,33(1):226-229.
作者姓名:徐翔  邹复民  廖律超  朱铨
作者单位:福建工程学院 下一代互联网技术应用研究开发中心, 福州 350108
基金项目:国家自然科学基金资助项目(61101139);福建省杰出青年基金资助项目(2012J06015);福建省重大专项专题项目(2011HZ0002-1)
摘    要:针对交通领域多源动态海量数据高性能计算的实时性、动态扩展性处理要求,提出了一种基于GemFire的分布式内存数据库实验平台。采用键-值数据存储结构和分布式动态成员关系,通过加载浮动车系统的真实数据在完整的云计算架构下,进行了计算性能测试与分析。实验结果表明,平台可将千万级以上大数据量的计算时间缩短至原系统的10%以内,满足了交通物联网云平台整合利用各子系统数据资源的应用需求。

关 键 词:海量数据    分布式内存数据库    云计算    动态扩展性
收稿时间:2012-07-16
修稿时间:2012-09-03

Experimental analysis for calculation performance of mass data based on GemFire
XU Xiang,ZOU Fumin,LIAO Lyvchao,ZHU Quan.Experimental analysis for calculation performance of mass data based on GemFire[J].journal of Computer Applications,2013,33(1):226-229.
Authors:XU Xiang  ZOU Fumin  LIAO Lyvchao  ZHU Quan
Affiliation:Research Center for Next-Generation Internet Technology and Applications, Fujian University of Technology, Fuzhou Fujian 350108, China
Abstract:With the demand of real-time and dynamic scalability processing for multi-source mass data in transportation, this paper proposed a distributed in-memory database experimental platform based on GemFire. The platform used the attributes of GemFire, such as key-value data storage structure and distributed dynamic membership. The actual data from floating car system was used to complete the performance analysis in cloud computing architecture. The experimental results show that the platform can shorten the calculation time of mass data to less than 10% of the existing system and basically satisfy the application requirements of transport data resources integration in cloud computing platform.
Keywords:mass data                                                                                                                          distributed in-memory database                                                                                                                          cloud computing                                                                                                                          dynamic scalability
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