CAJ | 학술논문

分析系统发育基因组工作流程并行处理性能，提出了一种适用于云计算平台中SciPhylomics执行的性能评估工作流程。首先，介绍了映射化简模型的应用实现Hadoop；然后，呈现了SciCumulus云工作流程引擎；最后，在亚马逊EC2云上使用两种并行执行方法（SciCumulus和Hadoop）实施工作流程。实验结果表明，尽管系统发育基因组学实验对计算环境要求严格，但实验仍然适合在云中执行。此外，所评估的工作流程呈现出几组数据密集型工作流程的许多特征，本方法可以扩展到其他实验类型。
분석계통발육기인조공작류정병행처이성능，제출료일충괄용우운계산평태중SciPhylomics집행적성능평고공작류정。수선，개소료영사화간모형적응용실현Hadoop；연후，정현료SciCumulus운공작류정인경；최후，재아마손EC2운상사용량충병행집행방법（SciCumulus화Hadoop）실시공작류정。실험결과표명，진관계통발육기인조학실험대계산배경요구엄격，단실험잉연괄합재운중집행。차외，소평고적공작류정정현출궤조수거밀집형공작류정적허다특정，본방법가이확전도기타실험류형。
The performance of parallel execution of phylogenomic tree is studied.A performance evaluation for SciPhylomics exe-cutions in a real cloud environment is proposed.Firstly,the Hadoop,a MapReduce model implementation is introduced.Then, the SciCumulus workflow engine is explained.Finally,the workflow is executed using two parallel execution approaches (SciCu-mulus and Hadoop)at the Amazon EC2 cloud.The experiment results demonstrate that the bioinformatics experiment is suitable to be executed in the cloud despite its need for high performance capabilities.Many features of the evaluated workflow are same as other data intensive workflows.Thus,proposed method could be used to analyze other experiments.