# Cloud Computing Based on Computational Characteristics for Disaster Monitoring
## 2 Related Work
### Computing Technology of Remote-Sensing Scientific Processing  
Remote-sensing scientific processing is a discipline in geoscience computing, and its development has undergone a similar process to that of geoscience computing. Geoscience computing is seen as an application of computational science to solve various types of problems in geography and geosciences [15]. Geoscience computing technology has produced new technologies and research with the development of computational science.  
At present, geoscience computing is usually a complex scientific computing issue. The objects processed are often large-scale Earth observation data, and the calculation process involves a large and complex calculation model. Massive amounts of Earth observation data and complex model calculations make disaster-monitoring model calculations both computationally and data-intensive. Therefore, the application of high-performance geoscience computing is divided into three categories: computing intensive, data-intensive, and network intensive [19].  
There are usually three situations in geoscientific computing applications. One is to build a complete data repository for stored multi-year Earth observation data, thereby using this long-term sequence database to study the application of changes in the state of the Earth system. The second is to study environmental monitoring on a global scale. This process requires analysis based on a large amount of data, both of which have requirements for the peak speed of calculation and the scale of problem solving. The third is disaster mitigation applications that deal with large-scale bursts of massive data, and require fast processing in real time or near real time. This process requires high computational efficiency. In summary, the computing problem has become the main bottleneck restricting the application of geoscience computing.  
Internet and network technologies are constantly evolving, and this greatly facilitates the effective management, sharing, and calculation of model resources for remote-sensing applications. The emergence of new computing technologies has helped solve many of the problems in geography and Earth science, for example, Earth system simulation, global change monitoring, and disaster prediction. The simulation implementation of these phenomena relies on new computing techniques to be truly realized.  
Distributed computing has been widely used for the processing of remote-sensing data, using a variety of distributed computing platforms such as clusters [20; 21], grids [22; 23] and clouds [24; 25; 26]. Distributed computing makes full use of idle distributed computing resources to provide online services in a unified manner. Grid and cluster computing are mainly focused on intensive resource sharing and applications. Cloud computing, the latest geoscience computing method, has evolved from parallel computing, distributed computing, and grid computing.  
Cloud computing is characterized by transparency, openness, scalability, and adaptability, providing the possibility to dynamically share and integrate various disaster-monitoring models. These Web service architectures facilitate the efficient sharing, reuse, and integration of distributed model resources.  
Extensive efforts have been applied to model services and remote-sensing data processing methods. These distributed computing methods improve the computational efficiency of disaster-monitoring models. However, most of the current computational studies have been developed for a specific algorithm, and no reusable computing model exists.