Experimental analysis of massive data computing performance based on GemFire

Experimental analysis of massive data computing performance based on GemFire

Xu Xiang, Shiming, Lio, Zhu Yu

A distributed Memory database experiment platform based on GemFire is proposed for the real-time and dynamic scalability processing requirements of high performance computing for multiple dynamic mass data in traffic field. Using the key-value data storage structure and the distributed dynamic member relationship, the computational performance of the floating vehicle system is tested and analyzed by loading the real data of the float car in the whole cloud computing architecture. The experimental results show that the platform can shorten the computation time of tens above large data to less than 10% of the original system, and satisfy the application demand of the data resources of each subsystem.

Keywords: massive data; Distributed memory Database; Cloud; Dynamic extensibility

Experimental analysis of massive data computing performance based on GemFire