Massive spatial data network service architecture based on double-cluster

Zhongmin Li; Lu Gao

doi:10.1117/12.837435

15 October 2009 Massive spatial data network service architecture based on double-cluster

Zhongmin Li, Lu Gao

Proceedings Volume 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining; 749245 (2009) https://doi.org/10.1117/12.837435
Event: International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, 2009, Wuhan, China

Abstract

It is the tendency for the development of massive spatial data network service to use cluster to enlarge load capacity of spatial data server. In this paper, we use the OSD (Object-based Storage Device) storage cluster as the shared storage of LVS (Linux Virtual Server) server cluster, and use the servers in the server pool of the LVS server cluster as the storage client of the OSD storage cluster, to build a scalable massive spatial data network service architecture, which uses the high scalability of the LVS server cluster and the OSD storage cluster to avoid the bottlenecks of massive spatial data network service bandwidth and storage I/O throughput. Several load balance scheduling algorithms embedded in the LVS server cluster can satisfy the demand of load balance in many applications. But those algorithms can't optimize load balance of spatial data servers, regardless of the features of spatial data. Spatial data in large scale network service application is generally organized according to the global longitude and latitude, and managed according to the principle "vertical hierarchies and horizontal dividing". According to the features of spatial data, we optimize the scheduling algorithm to enhance the Cache utilization efficiency for single spatial data server.

Citation Download Citation

Zhongmin Li and Lu Gao "Massive spatial data network service architecture based on double-cluster", Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 749245 (15 October 2009); https://doi.org/10.1117/12.837435

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