Abstract
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.
Highlights
The development of social network, sensor network, Internet of Things (IoT) and their multi-layer coupling data collection and recording technology makes the acquired spatio-temporal data of social world, computer world and material world have multimodal characteristics
The traditional spatio-temporal data visualization scheduling method of has been difficult to meet the requirements of diverse visualization applications of different terminals, as it is usually used for a single display task of desktop terminals
Due to the limitations of the traditional methods that lacks cloud-edge-terminal integrated scheduling model for multi-level visualization tasks, the self-adaptive scheduling mechanism of efficient collaborative scheduling for cloud-edge-terminal storage-computing-rendering resources is further studied in this paper
Summary
The development of social network, sensor network, Internet of Things (IoT) and their multi-layer coupling data collection and recording technology makes the acquired spatio-temporal data of social world, computer world and material world have multimodal characteristics. For the diverse and high-concurrency and high real-time application requirements of large-scale spatio-temporal data, optimizing the collaborative scheduling algorithm of resources under the cloud-edge-terminal hybrid architecture becomes very important (Chen et al, 2016; Shi et al, 2016). A new cloud-edge-terminal resources collaborative scheduling model for multi-level visualization of large-scale multi-modal data is proposed. We systematically study the integrated scheduling model of cloud-edge-terminal for multimodal spatiotemporal data multi-level visualization tasks. This model accurately depicts the hierarchical semantic mapping relationships between the diversified visualization application requirements of different terminals and scheduling tasks.
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