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Survey on Programming Models and Environments for Cluster, Cloud, and Grid Computing that defends Big Data

机译:群体,云和网格计算编程模型和环境调查,捍卫大数据

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A collection of interlinked stand-alone computers which function together in unity as a single incorporated computing resource is a kind of parallel or distributed processing systems called as the cluster. Clusters and grids are systems which intercommunicate among them and act as a single resource. This type of functionality can be referred to as the multi-computer parallel architecture that runs on certain considerations. Nevertheless cloud computing came forth as a more illustrious programming model to handle large data sets using clusters. A programming model is nothing but how data is carried out for handling the application. Performances, portability, objective architecture, sustainment of code are key measures that have to be commemorated while designing a programming model. Applications which are meant for data analytics generally deal with large data sets that undergo several stages of processing. Some of these stages are performed consecutively, and the others are carried out in parallel on clusters, grids, and cloud. This paper portrays a survey on how programming models which are developed for cluster cloud and grid act as a support for big data analytics. In addition, study on programming models which are currently being employed by leading multinational companies are pictured in the paper.
机译:作为单一合并计算资源一起统一一起函数的互通独立计算机的集合是一种并行或分布式处理系统称为群集。集群和网格是它们之间互通的系统,并充当单个资源。这种类型的功能可以称为在某些考虑中运行的多计算机并行架构。然而,云计算出现了一种更杰出的编程模型来处理使用群集的大数据集。编程模型只不过是如何执行数据来处理应用程序。性能,可移植性,客观架构,维持代码是在设计编程模型时必须纪念的关键措施。用于数据分析的应用程序通常处理经过几个处理阶段的大数据集。这些阶段中的一些连续进行,其他阶段在簇,网格和云上并行进行。本文描绘了关于集群云和网格开发的编程模型的调查,作为对大数据分析的支持。此外,目前正在通过领先的跨国公司雇用的编程模型的研究在论文中。

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