Skip to main content
SpringerLink
Log in

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

  • Chapter
  • First Online:
High Performance Computing for Geospatial Applications

Part of the book series: Geotechnologies and the Environment ((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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Askounis, D. T., Psychoyiou, M. V., & Mourtzinou, N. K. (2000). Using GIS and web-based technology to distribute land records: The case of Kallithea, Greece. Journal of Urban Technology, 7(1), 31–44. https://doi.org/10.1080/713684105

    Article  Google Scholar 

  • Batty, M., Hudson-Smith, A., Milton, R., & Crooks, A. (2010). Map mashups, web 2.0 and the GIS revolution. Annals of GIS, 16(1), 1–13. https://doi.org/10.1080/19475681003700831

    Article  Google Scholar 

  • Chen, Y., & Perrella, A. (2016). Interactive map to illustrate seat distributions of political party support levels: A web GIS application. Cartographica, 51(3), 147. https://doi.org/10.3138/cart.51.3.3288

    Article  Google Scholar 

  • Cheng, B., & Guan, X. (2016). Design and evaluation of a high-concurrency web map tile service framework on a high performance cluster. International Journal of Grid and Distributed Computing, 9(12), 127–142. https://doi.org/10.14257/ijgdc.2016.9.12.12

    Article  Google Scholar 

  • Cherradi, G., El Bouziri, A., Boulmakoul, A., & Zeitouni, K. (2017). Real-time HazMat environmental information system: A micro-service based architecture, vol. 109, pp, 982-987): Elsevier B.V, Amsterdam.

    Google Scholar 

  • Dangermond, J., & Goodchild, M. F. (2019). Building geospatial infrastructure. Geo-Spatial Information Science, 1–9. https://doi.org/10.1080/10095020.2019.1698274

  • DeJonghe, D. (2019). NGINX cookbook [second release]advanced recipes for high performance load balancing (1st edn).

    Google Scholar 

  • Felter, W., Ferreira, A., Rajamony, R., & Rubio, J. (2015, 29-31 March 2015). An updated performance comparison of virtual machines and Linux containers. In: Paper presented at the 2015 IEEE international symposium on performance analysis of systems and software (ISPASS).

    Google Scholar 

  • Fu, P., & Sun, J. (2010). Web GIS: Principles and applications. Redlands, CA: Esri Books.

    Google Scholar 

  • Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal, 69(4), 211–221. https://doi.org/10.1007/s10708-007-9111-y

    Article  Google Scholar 

  • Guiliani, G., Dubois, A., & Lacroix, P. (2013). Testing OGC web feature and coverage service performance: Towards efficient delivery of geospatial data. Journal of Spatial Information Science, 7, 1–23. https://doi.org/10.5311/JOSIS.2013.7.112

    Article  Google Scholar 

  • Hardie, A. (1998). The development and present state of web-GIS. Cartography, 27(2), 11–26. https://doi.org/10.1080/00690805.1998.9714273

    Article  Google Scholar 

  • Huang, Z., Das, A., Qiu, Y., & Tatem, A. (2012). Web-based GIS: The vector-borne disease airline importation risk (VBD-AIR) tool. International Journal of Health Geographics, 11(1), 33. https://doi.org/10.1186/1476-072X-11-33

    Article  Google Scholar 

  • Khan, A. (2017). Key characteristics of a container orchestration platform to enable a modern application. IEE Cloud Computing, 4(5), 42–48. https://doi.org/10.1109/MCC.2017.4250933

    Article  Google Scholar 

  • Krygier, J. (1999). World wide web mapping and GIS: An application for public participation. Cartographic Perspectives, 33, 66–67. https://doi.org/10.14714/CP33.1023

    Article  Google Scholar 

  • Neumann, A. (2008). Web mapping and web cartography. In S. Shekhar & H. Xiong (Eds.), Encyclopedia of GIS (pp. 1261–1269). Boston, MA: Springer US.

    Chapter  Google Scholar 

  • NSF. (2007). Cyberinfrastructure Vision for 21st Century Discovery.

    Google Scholar 

  • Open Geospatial Consortium. (2020). Welcome to The Open Geospatial Consortium. Retrieved from https://www.opengeospatial.org/

  • Pahl, C., Brogi, A., Soldani, J., & Jamshidi, P. (2017). Cloud container technologies: A state-of-the-art review. IEEE Transactions on Cloud Computing, 1–1. https://doi.org/10.1109/TCC.2017.2702586

  • Pahl, C., & Lee, B. (2015). Containers and clusters for edge cloud architectures – A technology review. In: Paper presented at the 2015 3rd international conference on future internet of things and cloud, Rome, Italy.

    Google Scholar 

  • Peng, Z.-R., & Tsou, M.-H. (2003). Internet GIS: Distributed geographic information Services for the Internet and Wireless Networks. New York: Wiley.

    Google Scholar 

  • RapiĹ„ski, J., Bednarczyk, M., & Zinkiewicz, D. (2019). JupyTEP IDE as an online tool for earth observation data processing. Remote Sensing, 11(17). https://doi.org/10.3390/rs11171973

  • Tosatto, A., Ruiu, P., & Attanasio, A. (2015). Container-based orchestration in cloud: State of the art and challenges. In: Paper presented at the ninth international conference on complex, intelligent, and software intensive systems, Blumenau, Brazil.

    Google Scholar 

  • Traefik. (2019). Basics. Retrieved from https://docs.traefik.io/v1.7/basics/#load-balancing

  • Veenendaal, B., Brovelli, M., & Li, S. (2017). Review of web mapping: Eras, trends and directions. ISPRS International Journal of Geo-Information, 6(10), 317. https://doi.org/10.3390/ijgi6100317

    Article  Google Scholar 

  • Wang, S., Zhong, Y., & Wang, E. (2019). An integrated GIS platform architecture for spatiotemporal big data. Future Generation Computer Systems, 94, 160–172. https://doi.org/10.1016/j.future.2018.10.034

    Article  Google Scholar 

  • Wilkinson, B., & Allen, M. (2005). Parallel programming (2nd ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

    Google Scholar 

  • Wu, H., Guan, X., Liu, T., You, L., & Li, Z. (2013). A high-concurrency web map tile service built with open-source software. In Modern accelerator technologies for geographic information science (pp. 183–195). Boston, MA: Springer.

    Chapter  Google Scholar 

  • Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big data and cloud computing: Innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13–53. https://doi.org/10.1080/17538947.2016.1239771

    Article  Google Scholar 

  • Yang, C., Wu, H., Huang, Q., Li, Z., Li, J., Li, W., et al. (2011). WebGIS performance issues and solutions. In D. V. Li (Ed.), Advances in web-based GIS, mapping services and applications (pp. 121–138). London: Taylor & Francis Group.

    Chapter  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Center for Applied Geographic Information Science, Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC, USA

    Zachery Slocum & Wenwu Tang

Authors
  1. Zachery Slocum
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenwu Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zachery Slocum .

Editor information

Editors and Affiliations

  1. Center for Applied Geographic Information Science, Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC, USA

    Wenwu Tang

  2. Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Shaowen Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Slocum, Z., Tang, W. (2020). Integration of Web GIS with High-Performance Computing: A Container-Based Cloud Computing Approach. In: Tang, W., Wang, S. (eds) High Performance Computing for Geospatial Applications. Geotechnologies and the Environment, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-030-47998-5_8

Download citation

Publish with us

Policies and ethics

Search

Navigation