Visible to the public A Visual MapReduce Program Development Environment for Heterogeneous Computing on Clouds

TitleA Visual MapReduce Program Development Environment for Heterogeneous Computing on Clouds
Publication TypeConference Paper
Year of Publication2018
AuthorsLiang, Tyng-Yeu, Yeh, Li-Wei, Wu, Chi-Hong
Conference NameProceedings of the 2018 International Conference on Computing and Data Engineering
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-6393-8
Keywordscomposability, Google Blockly, Hadoop, Heterogeneous computing, MapReduce, Metrics, Outsourced Database Integrity, pubcrawl, Resiliency, XML-to-MR translator
AbstractThis paper is aimed at proposing a visual MapReduce program development environment called VMR for heterogeneous computing on Clouds. This development environment mainly has three advantages as follows. First, it allows users to drag and drop graphical blocks instead of text typing for editing programs. Therefore, users can save their effort and time spent on MapReduce programming especially when they analyze data on clouds through mobile devices. Second, it can automatically translate the blocks of users' MapReduce programs into three different versions including Java, C and CUDA of source codes, and select one of these three versions according to the processor architecture of allocated resources for execution. Consequently, users can transparently and effectively exploit heterogeneous resources in clouds for executing their MapReduce programs while they has no need to individually write programs for each of different processor architectures by themselves. Third, it can enable clouds to outsource the computation tasks of MapReduce programs to mobile devices in order for increasing job throughput or program performance.
URLhttp://doi.acm.org/10.1145/3219788.3219800
DOI10.1145/3219788.3219800
Citation Keyliang_visual_2018