Private cloud computing system realization method adaptable to data and computing intensive tasks
一种适应数据与计算密集型任务的私有云系统实现研究

Compared with public cloud computing systems, private cloud computing systems aiming at data and computing dual intensive tasks have higher demand in computing and management efficiency. The realization methods for public cloud computing system are too complicated for users to develop. A simplify and easy to use realization of private cloud computing system is requirement. To meet this requirement, this paper proposed an approach to build a private cloud computing system which was able to adapt both data and computing intensive tasks on basis of public cloud computing systems implementation. This approach used job files with aim of describing computing tasks and determined the input and output files of computing model. Computing model of data processing could be reflected more intuitively by simplify the Google MapReduce parallel computing framework; the use of connecting computing flow automatically made the approach more streamline and rapidly to process intensive tasks. Experiment results shows that this approach can reduce extra computation overhead and improve processing efficiency significantly. This approach offers a high practical value.