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

机译:防御大数据的集群,云和网格计算的编程模型和环境调查

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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:相互链接的独立计算机的集合,它们作为单个合并的计算资源一起共同起作用,是一种称为群集的并行或分布式处理系统。集群和网格是在它们之间相互通信并充当单个资源的系统。这种类型的功能可以称为基于某些考虑因素运行的多计算机并行体系结构。然而,云计算作为一种更为出色的编程模型而出现,可以使用集群来处理大型数据集。编程模型不过是如何处理应用程序而执行数据处理。性能,可移植性,目标体系结构,代码维护是设计编程模型时必须记住的关键指标。用于数据分析的应用程序通常会处理经历多个处理阶段的大型数据集。这些阶段中的某些阶段是连续执行的,而其他阶段则是在集群,网格和云上并行执行的。本文描述了一项针对集群云和网格开发的编程模型如何支持大数据分析的调查。此外,本文还介绍了对目前领先的跨国公司正在采用的编程模型的研究。

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