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Integration of Web GIS with High-Performance Computing: A Container-Based Cloud Computing Approach

  • Zachery SlocumEmail author
  • Wenwu Tang
Chapter
  • 141 Downloads
Part of the Geotechnologies and the Environment book series (GEOTECH, volume 23)

Abstract

In this chapter, we present a Web GIS framework, called GeoWebSwarm, which is driven by containers-based cloud computing technologies. Web GIS applications have been widely used for the dissemination of spatial data and knowledge. However, the computationally intensive nature of these applications prevents the use of Web GIS to explore large spatial data when using traditional single-server paradigms—i.e., a big data challenge. Containers as a service (CaaS) are a potential solution to implementing responsive and reliable Web GIS applications while handling big data. CaaS is made possible through cyberinfrastructure-enabled high-performance computing. Our container-based framework is designed using container orchestration to integrate high-performance computing with Web GIS, which results in improvements on the capacity and capability of Web GIS over single-server deployments. Map tile requests are distributed using a load balancing approach to multiple Web GIS servers through cloud computing based technologies. Through experiments measuring real-time user request performance of multiple Web GIS containers, we demonstrate significant computing performance benefits in response time and concurrent capacity. Utilizing the GeoWebSwarm framework, Web GIS can be efficiently used to explore and share geospatial big data.

Keywords

Big Data Container Orchestration Containers as a Service Docker 

Notes

Acknowledgement

We would like to thank Dr. Elizabeth Delmelle and Dr. Eric Delmelle for their guidance as members of the capstone committee of the first author on which this work is based. We are also indebted to the anonymous reviewers for their insightful comments and suggestions. The authors also recognize the Center for Applied Geographic Information Science at UNC Charlotte for providing the computing resources to make this work possible.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Center for Applied Geographic Information Science, Department of Geography and Earth SciencesUniversity of North Carolina at CharlotteCharlotteUSA

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