Enabling BigData Platform for MapReduce Applications in the Science Cloud
Yunhee Kang · 2014
Scientific data experiments based on simulations create vast data stores that require new scientific methods to analyze and organize the data. The arrival of BigData science in the last two decades constitutes another scientific revolution. To handle BigData issues, cloud computing has gained significant traction in recent years. MapReduce is a practical and attractive programming model for parallel data processing in high-performance clusters virtualized of the cloud. Especially open source based MapReduce implementations are located as de facto standards in the science cloud projects. In this article we introduce research projects including LHC, PolarGrid and FutureGrid. We also describe software platforms including MapReduce framework and its extensions used for handling BigData problems in their projects