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Parallel computing can improve the data-processing efficiency significantly. However, the traditional approaches, such as MPI and MapReduce, need to program in the special environment. In this paper, a new distributed computing framework named MEX is proposed. Users just provides the input files and the name of an executable program to MEX. Then MEX will automatically process these files on a cluster...
Optimizing the Map Phase is an important way to reduce the MapReduce (MR) job runtime. The common way for such studies is to design more efficient scheduling policies. However, there exists a kind of MapReduce applications, named imprecise applications, where the reduce phase can be completed based on the outputs of part of the map tasks. According to the feature of imprecise applications, we propose...
Building distributed applications is difficult mostly because of concurrency management. Existing approaches primarily include events and threads. Researchers and developers have been debating for decades to prove which is superior. Although the conclusion is far from obvious, this long debate clearly shows that neither of them is perfect. One of the problems is that they are both complex and error-prone...
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