توضیحات
Scientific workflow management systems (SWFMSs) have been widely adopted in Physics, Astronomy, Bioinformatics, Neuroscience, Earth Science, and Social Science to provide essential support to scientific computing, including management of data and task dependencies, job scheduling and execution, provenance tracking, etc. Nevertheless, the big data era has raised unprecedented challenges against the solution to data processing based on traditional scientific workflows, as the data scale and computation complexity are growing exponentially. The ETL (Extraction-Transformation-Loading), storage, retrieval, analysis and application upon the huge amounts of data are beyond the capability of traditional data processing infrastructures.As an emerging computing paradigm, Cloud computing[6] has brought us tremendous convenience for the processing of large-scale datasets. Hadoop[22], together with associated systems in its ecosystem, has been widely adopted to solve the big data problem arisen in both scientific community andenterprises, as they provide a scalable and large-scale solution to data storage, analysis and mining. Theoretically, to address the big data problems in these areas, scientists and application developers may simply refactor all the existed workflow applications into the Cloud computing paradigm, which looks straightforward but in reality impractical. As traditional scientific workflow applications have been mature during many years’ development and always involve complicated application logic and consist of massive computing processes, including organization, distribution, coordination and parallel processing. Transforming these scientific workflows will not only cost scientists and developers much time, but also manually handle all the integration details with various underlying Cloud platforms
by :Yong Zhao, Youfu Li,Shiyong Lu
File information : language English, 9 page, size:864 k
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کاری از :Yong Zhao, Youfu Li,Shiyong Lu
اطلاعات فایل :زبان انگلیسی /9صفحه /حجم :864 k
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