摘要
云计算的后台处理技术是云计算系统的总体保障。本文主要介绍当前云计算中的后台大规模数据处理技术。大规模数据处理技术对于云计算的后台数据收集与整理起着关键的作用。在云计算中的大规模数据处理技术主要解决三个问题,即可靠性,可扩展性以及易编程性。本文结合Google的MapReduce编程方式,以及微软的Dryad编程方式,着重介绍在实践中,如何达到上述的目标。
Data processing technologies used in servers are the critical foundations for the cloud computing systems. This paper introduces the large scale data processing technologies used in the current cloud computing implementations. Such technologies are quite important for the data collections as well as processing. There are three key goals for processing the large scale data i.e. reliability, scalability and easy to program. Based on the Map Reduce programming paradigm from Google and Dryad model from Microsoft, how to achieve the goals in practice are discussed in detail.
出处
《电信工程技术与标准化》
2009年第11期12-16,共5页
Telecom Engineering Technics and Standardization
基金
国家重点基础研究发展计划(973计划)(2007CB310900)
国家自然科学基金(NSFC90718040)
国家高技术研究发展(863)计划(2008AA01Z112)
关键词
云计算
分布式系统
数据处理
编程模式
cloud computing, distributed system, data processing, programming model
