Runtimes and optimizations for map reduce
Cairong Yan, Yongfeng Huang, Guangwei Xu · IETE Technical Review · 2013
AbstractMap Reduce is emerging as an important programming model for data-intensive applications. It is widely used in a variety of different environments to deal with the services of big data processing. In order to adapt to these platforms, the original implementation of Map Reduce framework has been extended and enhanced. This paper describes a comprehensive taxonomy of Map Reduce runtimes for different computing environments ranging from traditional homogeneous PC cluster server to dynamic, virtual, heterogeneous, and mobile computing platform, aiming at a good understanding of wide applicability of this model. It also provides a survey for the optimization strategies applied in these runtimes and that are gaining a lot of momentum in both research and industrial communities. The taxonomy and survey is used to identify and expand the fields of application for MapReduce model and provide references for research.