Computation Capability Deduction Architecture for MapReduce on Cloud Computing

This is the authors' abstract. We don't add key points for this paper.

MapReduce gradually becomes the de facto programming standard of applications on cloud computing. However, MapReduce needs a cloud administrator to manually configure parameters of the run-time system such as slot numbers for Map and Reduce tasks in order to get the best performance. Because the manual configuration has a risk of performance degradation, MapReduce should utilize the Computation Capability Deduction Architecture (CCDA) proposed in this paper to avoid the risk. MapReduce can use CCDA to help the run-time system to distribute appropriate numbers of tasks over computers in a cloud at run time without any manual configuration made by a cloud administrator. According to experiment observations in this paper, MapReduce can get great performance improvement with the help of CCDA in data-intensive applications such as Inverted Index and Word Count that are usually required to process big data on cloud computing.