Honolulu, HI, USA USA
June 24, 2012 to June 29, 2012
Spatial overlay processing is a widely used compute- intensive GIS application that involves aggregation of two or more layers of maps to facilitate intelligent querying on the collocated output data. When large GIS data sets are represented in polyg- onal (vector) form, spatial analysis runs for extended periods of time, which is undesirable for time-sensitive applications such as emergency response. We have, for the first time, created an open-architecture-based system named Crayons for Azure cloud platform using state-of-the-art techniques. During the course of development of Crayons system, we faced numerous challenges and gained invaluable insights into Azure cloud platform, which are presented in detail in this paper. The challenges range from limitations of cloud storage and computational services to the choices of tools and technologies used for high performance computing (HPC) application design. We report our findings to provide concrete guidelines to an eScience developer for 1) choice of persistent data storage mechanism, 2) data structure representation, 3) communication and synchronization among nodes, 4) building robust failsafe applications, and 5) optimal cost-effective utilization of resources. Our insights into each challenge faced, the solution to overcome it, and the discussion on the lessons learnt from each challenge can be of help to eScience developers starting application development on Azure and possibly other cloud platforms.
Cloud computing, Data structures, Libraries, Guidelines, Memory, Geographic information systems, Irregular computations, Engineering HPC applications on Azure cloud, Scientific applications on cloud, Cloud computing, Parallel overlay operations, Data-intensive applications
Dinesh Agarwal, S. K. Prasad, "Lessons Learnt from the Development of GIS Application on Azure Cloud Platform", CLOUD, 2012, 2013 IEEE Sixth International Conference on Cloud Computing, 2013 IEEE Sixth International Conference on Cloud Computing 2012, pp. 352-359, doi:10.1109/CLOUD.2012.140