The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 477–483, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-477-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 477–483, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-477-2020

  25 Aug 2020

25 Aug 2020

A NEW CLOUD-EDGE-TERMINAL RESOURCES COLLABORATIVE SCHEDULING FRAMEWORK FOR MULTI-LEVEL VISUALIZATION TASKS OF LARGE-SCALE SPATIO-TEMPORAL DATA

X. M. Li, W. X. Wang, S. J. Tang, J. Z. Xia, Z. G. Zhao, Y. Li, Y. Zheng, and R. Z. Guo X. M. Li et al.
  • Research Institute for Smart Cities & Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, P.R. China

Keywords: Multi-modal spatio-temporal data, Cloud-edge-terminal, Multi-level visualization tasks, Storage-computing-rendering, Collaborative scheduling, Flexible allocation strategy, Adaptive optimization mechanism

Abstract. To address the multi-modal spatio-temporal data efficient scheduling problem of the diverse and highly concurrent visualization applications in cloud-edge-terminal environment, this paper systematically studies the cloud-edge-terminal integrated scheduling model of multi-level visualization tasks of multi-modal spatio-temporal data. By accurately defining the hierarchical semantic mapping relationship between the diverse visual application requirements of different terminals and scheduling tasks, we propose a multi-level task-driven cloud-edge-terminal multi-granularity storage-computing-rendering resource collaborative scheduling method. Based on the workflow, the flexible allocation strategy of cloud-edge-terminal scheduling service chain that consider the characteristics of spatio-temporal task is constructed. Finally, we established a cloud-edge-terminal scheduling adaptive optimization mechanism based on the service quality evaluation model, and developed a prototype system. Experiments are conducted with the urban construction and construction management, the results show that the new method breaks through the bottleneck of traditional spatio-temporal data visualization scheduling, and it can provide theoretical and methodological support for the visualization and scheduling of spatio-temporal big data.