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International Journal of Digital Earth Volume 8, 2015 - Issue 6
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Original Articles

Adopting cloud computing to optimize spatial web portals for better performance to support Digital Earth and other global geospatial initiatives

Jizhe XiaDepartment of Geography and GeoInformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USAView further author information
,
Chaowei YangDepartment of Geography and GeoInformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USACorrespondence[email protected]
View further author information
,
Kai LiuDepartment of Geography and GeoInformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USAView further author information
,
Zhipeng GuiDepartment of Geography and GeoInformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USA;School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, P.R. ChinaView further author information
,
Zhenlong LiDepartment of Geography and GeoInformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USAView further author information
,
Qunying HuangDepartment of Geography and GeoInformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USAView further author information
&
Rui LiDepartment of Geography and GeoInformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USA;LIESMARS, Wuhan University, Wuhan, Hubei, P.R. ChinaView further author information
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Pages 451-475 | Received 29 Dec 2013, Accepted 27 May 2014, Published online: 30 Jun 2014

Figures & data

Figure 1. Concurrent, computing, and communication intensities in SWP.
Figure 1. Concurrent, computing, and communication intensities in SWP.
Figure 2. The distribution of SWP end users and geospatial resources across the world.
Figure 2. The distribution of SWP end users and geospatial resources across the world.
Figure 3. Cloud-enabled framework for SWP.
Figure 3. Cloud-enabled framework for SWP.
Figure 4. Cloud workload balancer architecture.
Figure 4. Cloud workload balancer architecture.

Algorithm 1. SWP load balancing in the cloud.

Figure 5. Elasticity manager architecture.
Figure 5. Elasticity manager architecture.

Algorithm 2. Threshold-based SWP auto-scaling in the cloud.

Algorithm 3. Threshold-based SWP global deployment in the cloud.

Figure 6. Geographic locations of the three cloud services and the traditional server.
Figure 6. Geographic locations of the three cloud services and the traditional server.

Table 1. Configurations of VMs and the traditional server.

Figure 7. Test scenarios across cloud services (adapted from Yang et al. Citation2014, Chapter 12, Section 12.3.3).
Figure 7. Test scenarios across cloud services (adapted from Yang et al. Citation2014, Chapter 12, Section 12.3.3).
Figure 8. The SWP response time on cloud and traditional server.
Figure 8. The SWP response time on cloud and traditional server.
Figure 9. The SWP mean response time as a function of the concurrent request number with the support of the cloud workload balancer.
Figure 9. The SWP mean response time as a function of the concurrent request number with the support of the cloud workload balancer.
Figure 10. SWP response and elasticity manager reaction to a dynamic workload.
Figure 10. SWP response and elasticity manager reaction to a dynamic workload.
Figure 11. The SWP mean response time as a function of global deployment.
Figure 11. The SWP mean response time as a function of global deployment.
Figure 12. Service response time as a function of concurrent request number from different geo locations.
Figure 12. Service response time as a function of concurrent request number from different geo locations.

Table 2. Key cloud features to address the SWP challenges.

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