大数据算法的并行化和部署:TOREADOR方法

Ivan Martinez, J. Montero, T. Lobo, B. D. Martino, Salvatore D'Angelo, A. Esposito
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引用次数: 2

摘要

为了减少中小企业(sme)为获得必要的专业知识、硬件和软件来运行适当的大数据分析所需的初始投资,TOREADOR提出了一个大数据分析框架,支持用户通过将固有成本降至最低来设计自己的大数据解决方案。TOREADOR框架的目标之一是支持开发人员并行化和部署他们的算法,以便开发他们自己的分析解决方案。本文描述了由CINI开发并在TOREADOR框架内采用的基于代码的方法,以并行化用户的算法并将其部署到分布式平台上,重点介绍了它与ATOS提供的web服务和资源的集成,以实现解决方案的实际部署。
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Parallelization and Deployment of Big Data Algorithms: The TOREADOR Approach
In order to reduce the initial investments needed by small and medium enterprises (SMEs) to acquire the necessary expertise, hardware and software to run proper Big Data Analytics, TOREADOR proposes a Big Data Analytics framework which supports users in devising their own Big Data solutions by keeping the inherent costs at a minimum. Among the objectives of the TOREADOR framework is supporting developers in parallelizing and deploying their algorithms, in order to develop they own analytics solutions. This paper describes the Code-Based approach, developed by CINI and adopted within the TOREADOR framework to parallelize users' algorithms and deploy them on distributed platforms, with a focus on its integration with the web services and resources offered by ATOS for the actual deployment of the solution.
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