Author： Huang Wei Zhang Wen Zhang Dongying Meng Lingkui
Publisher： MDPI
E-ISSN： 2220-9964|6|3|84-84
ISSN： 2220-9964
Source： ISPRS International Journal of Geo-Information, Vol.6, Iss.3, 2017-03, pp. : 84-84
Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.
Abstract
Geospatial big data analysis (GBDA) is extremely significant for time-constraint applications such as disaster response. However, the time-constraint analysis is not yet a trivial task in the cloud computing environment. Spatial query processing (SQP) is typical computation-intensive and indispensable for GBDA, and the spatial range query, join query, and the nearest neighbor query algorithms are not scalable without using MapReduce-liked frameworks. Parallel SQP algorithms (PSQPAs) are trapped in screw-processing, which is a known issue in Geoscience. To satisfy time-constrained GBDA, we propose an elastic SQP approach in this paper. First, Spark is used to implement PSQPAs. Second, Kubernetes-managed Core Operation System (CoreOS) clusters provide self-healing Docker containers for running Spark clusters in the cloud. Spark-based PSQPAs are submitted to Docker containers, where Spark master instances reside. Finally, the horizontal pod auto-scaler (HPA) would scale-out and scale-in Docker containers for supporting on-demand computing resources. Combined with an auto-scaling group of virtual instances, HPA helps to find each of the five nearest neighbors for 46,139,532 query objects from 834,158 spatial data objects in less than 300 s. The experiments conducted on an OpenStack cloud demonstrate that auto-scaling containers can satisfy time-constraint GBDA in clouds.
Related content
Access to resources
Recommend
Access to resources
By Zhao Yanli Padmanabhan Anand Wang Shaowen
International Journal of Geographical Information Science, Vol. 27, Iss. 2, 2013-02 ,pp. :
By Zhou Dongbo Jiang Gang Yu Jie Liu Leyuan Li Wenbo
ISPRS International Journal of Geo-Information, Vol. 6, Iss. 3, 2017-03 ,pp. :