Model-Driven Approach to Hadoop Deployment in Cloud

Zheyi Chen, Tao Xiang, Xing Chen
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Abstract

Due to the diversification of software and hardware resources in cloud and ever-changing demand for deployment, Hadoop deployment is faced with great challenges in difficulty and complexity. Most of present researches lie in environment configuration and parameter setting of deployment, they do not take into account diversification of infrastructure and scalability issues in cloud. In order to quickly customize, deploy and expand Hadoop services according to the demand, a model-driven approach to Hadoop deployment is proposed in the paper. Firstly, Hadoop demand and deployment models are presented. Secondly, the transformation method from the demand model to the deployment model is proposed. Thirdly, the bidirectional synchronization between Hadoop deployment model and running system is realized based on runtime model.
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模型驱动的Hadoop云部署方法
由于云环境下软硬件资源的多样化和部署需求的不断变化,Hadoop的部署在难度和复杂性上都面临着巨大的挑战。目前的研究大多停留在环境配置和部署参数设置上,没有考虑到云环境下基础设施的多样化和可扩展性问题。为了根据需求快速定制、部署和扩展Hadoop服务,本文提出了一种模型驱动的Hadoop部署方法。首先,提出了Hadoop的需求模型和部署模型。其次,提出了从需求模型到部署模型的转换方法。第三,基于运行时模型实现Hadoop部署模型与运行系统的双向同步。
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