Cloud Computing Based on Computational Characteristics for Disaster Monitoring
Resources related to remote-sensing data, computing, and models are scattered globally. The use of remote-sensing images for disaster-monitoring applications is data-intensive and involves complex algorithms. These characteristics make the timely and rapid processing of disaster-monitoring applicati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/19/6676 |
| _version_ | 1797552744793374720 |
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| author | Quan Zou Guoqing Li Wenyang Yu |
| author_facet | Quan Zou Guoqing Li Wenyang Yu |
| author_sort | Quan Zou |
| collection | DOAJ |
| description | Resources related to remote-sensing data, computing, and models are scattered globally. The use of remote-sensing images for disaster-monitoring applications is data-intensive and involves complex algorithms. These characteristics make the timely and rapid processing of disaster-monitoring applications challenging and inefficient. Cloud computing provides a dynamically scalable resource over the Internet. The rapid development of cloud computing has led to an increase in the computational performance of data-intensive computing, providing powerful throughput by distributing computation across many distributed computers. However, the use of current cloud computing models in scientific applications using remote-sensing image data has been limited to a single image-processing algorithm rather than a well-established model and method. This poses problems for the development of complex disaster-monitoring applications on cloud platform architectures. For example, distributed computing strategies and remote-sensing image-processing algorithms are highly coupled and not reusable. The aims of this paper are to identify computational characteristics of various disaster-monitoring algorithms and classify them according to different computational characteristics; explore a reusable processing model based on the MapReduce programming model for disaster-monitoring applications; and then establish a programming model for each type of algorithm. This approach provides a simpler programming method for programmers to implement disaster-monitoring applications. Finally, some examples are given to explain the proposed method and test its performance. |
| first_indexed | 2024-03-10T16:05:29Z |
| format | Article |
| id | doaj.art-4241694cd6f94ba9a62b2a95dd7706ae |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T16:05:29Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4241694cd6f94ba9a62b2a95dd7706ae2023-11-20T14:56:17ZengMDPI AGApplied Sciences2076-34172020-09-011019667610.3390/app10196676Cloud Computing Based on Computational Characteristics for Disaster MonitoringQuan Zou0Guoqing Li1Wenyang Yu2School of Computer Information and science, Centre for Research and Innovation in Software Engineering, Southwest University, Chongqing 400715, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaResources related to remote-sensing data, computing, and models are scattered globally. The use of remote-sensing images for disaster-monitoring applications is data-intensive and involves complex algorithms. These characteristics make the timely and rapid processing of disaster-monitoring applications challenging and inefficient. Cloud computing provides a dynamically scalable resource over the Internet. The rapid development of cloud computing has led to an increase in the computational performance of data-intensive computing, providing powerful throughput by distributing computation across many distributed computers. However, the use of current cloud computing models in scientific applications using remote-sensing image data has been limited to a single image-processing algorithm rather than a well-established model and method. This poses problems for the development of complex disaster-monitoring applications on cloud platform architectures. For example, distributed computing strategies and remote-sensing image-processing algorithms are highly coupled and not reusable. The aims of this paper are to identify computational characteristics of various disaster-monitoring algorithms and classify them according to different computational characteristics; explore a reusable processing model based on the MapReduce programming model for disaster-monitoring applications; and then establish a programming model for each type of algorithm. This approach provides a simpler programming method for programmers to implement disaster-monitoring applications. Finally, some examples are given to explain the proposed method and test its performance.https://www.mdpi.com/2076-3417/10/19/6676programming modeldisaster monitoringcomputational characteristicsremote-sensing data |
| spellingShingle | Quan Zou Guoqing Li Wenyang Yu Cloud Computing Based on Computational Characteristics for Disaster Monitoring Applied Sciences programming model disaster monitoring computational characteristics remote-sensing data |
| title | Cloud Computing Based on Computational Characteristics for Disaster Monitoring |
| title_full | Cloud Computing Based on Computational Characteristics for Disaster Monitoring |
| title_fullStr | Cloud Computing Based on Computational Characteristics for Disaster Monitoring |
| title_full_unstemmed | Cloud Computing Based on Computational Characteristics for Disaster Monitoring |
| title_short | Cloud Computing Based on Computational Characteristics for Disaster Monitoring |
| title_sort | cloud computing based on computational characteristics for disaster monitoring |
| topic | programming model disaster monitoring computational characteristics remote-sensing data |
| url | https://www.mdpi.com/2076-3417/10/19/6676 |
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