Massive GIS database system with autonomic resource management

Yun Lu, Ming Zhao, Guangqiang Zhao, Lixi Wang, Naphtali Rishe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

GIS application hosts are becoming more and more complicated. Thus, their management is more time consuming, and reliability decreases with the complexity of GIS applications increasing. We have designed, implemented, and evaluated, a virtualized whole Large Scale Distributed Spatial Data Visualization System for optimizing maintainability and performance when handling large amount of GIS data. We employ the virtual machines (VMs) technique, load balance cluster techniques, and autonomic resource management to improve the system's performance. The proposed system was prototyped on TerraFly [1], a production web map service, and evaluated using actual TerraFly workloads. The results show that the virtual TerraFly system has both good performance and much better maintainability. Our experiments show that the proposed Virtual TerraFly Geo-database system has doubled the reliability, and saved 20-30% computing resources cost compared to current static peak-load physical machine node allocations.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013
PublisherIEEE Computer Society
Pages451-456
Number of pages6
Volume2
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 - Miami, FL, United States
Duration: Dec 4 2013Dec 7 2013

Other

Other2013 12th International Conference on Machine Learning and Applications, ICMLA 2013
CountryUnited States
CityMiami, FL
Period12/4/1312/7/13

Fingerprint

Geographic information systems
Maintainability
Data visualization
Costs
Experiments
Virtual machine

Keywords

  • Database Systems
  • GIS
  • maintainability
  • Performance

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

Cite this

Lu, Y., Zhao, M., Zhao, G., Wang, L., & Rishe, N. (2013). Massive GIS database system with autonomic resource management. In Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 (Vol. 2, pp. 451-456). [6786152] IEEE Computer Society. https://doi.org/10.1109/ICMLA.2013.161

Massive GIS database system with autonomic resource management. / Lu, Yun; Zhao, Ming; Zhao, Guangqiang; Wang, Lixi; Rishe, Naphtali.

Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013. Vol. 2 IEEE Computer Society, 2013. p. 451-456 6786152.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lu, Y, Zhao, M, Zhao, G, Wang, L & Rishe, N 2013, Massive GIS database system with autonomic resource management. in Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013. vol. 2, 6786152, IEEE Computer Society, pp. 451-456, 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013, Miami, FL, United States, 12/4/13. https://doi.org/10.1109/ICMLA.2013.161
Lu Y, Zhao M, Zhao G, Wang L, Rishe N. Massive GIS database system with autonomic resource management. In Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013. Vol. 2. IEEE Computer Society. 2013. p. 451-456. 6786152 https://doi.org/10.1109/ICMLA.2013.161
Lu, Yun ; Zhao, Ming ; Zhao, Guangqiang ; Wang, Lixi ; Rishe, Naphtali. / Massive GIS database system with autonomic resource management. Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013. Vol. 2 IEEE Computer Society, 2013. pp. 451-456
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