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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Vydané v:Applied sciences Ročník 10; číslo 19; s. 6676
Hlavní autori: Zou, Quan, Li, Guoqing, Yu, Wenyang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.10.2020
Predmet:
Algorithms
Cloud computing
computational characteristics
disaster monitoring
Disasters
Distributed processing
Earth science
Emergency management
Environmental monitoring
Internet
Methods
programming model
Real time
Remote sensing
remote-sensing data
Satellites
Sensors
Software
Technology application
China
ISSN:2076-3417, 2076-3417
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app10196676