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Open AccessArticle

Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Author to whom correspondence should be addressed.
Academic Editors: Ozgun Akcay and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(3), 84;
Received: 12 January 2017 / Revised: 2 March 2017 / Accepted: 13 March 2017 / Published: 15 March 2017
PDF [3283 KB, uploaded 15 March 2017]


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. View Full-Text
Keywords: elasticity; spatial query processing; Spark; container; Kubernetes; OpenStack elasticity; spatial query processing; Spark; container; Kubernetes; OpenStack

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Huang, W.; Zhang, W.; Zhang, D.; Meng, L. Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis. ISPRS Int. J. Geo-Inf. 2017, 6, 84.

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