# Cloud Computing Based on Computational Characteristics for Disaster Monitoring
## 3 Cloud Computing Model for Disaster Monitoring
### Application of MapReduce for Disaster-Monitoring Model  
The MapReduce programming model designed by Google provides a simple and easy-to-use computing model for users, which facilitates collaborative computing of services. It is a linear scalable programming model for large-scale data processing and simplified distributed computing. It handles complex cluster processing problems such as reliability, scalability, and availability. All internal processing processes are transparent to developers, thus realizing parallel programmability. Therefore, developers do not need to pay attention to the underlying details of the system, and only need to focus on the application itself, which is more convenient for parallel processing of massive amounts of data. This computing model greatly simplifies the parallel processing of mass data on distributed cluster. The MapReduce distributed computing framework is based on parallel processing of data partitioning and task decomposition, which makes it easier to process large-scale data sets in parallel in a distributed cluster. The idea of MapReduce is to decompose the tasks to be executed. The Map operation acts on each element in the independent element group as required. This operation is independent, and then a new element group is saved to store the intermediate result just generated. Because the sets of elements are independent, the mapping operations are essentially highly parallel. The Reduce operation properly merges elements within an element group. Although it is possible that the parallelism of the protocol operation is not as strong as that of the mapping operation, large-scale operations may still be relatively independent. Due to the weak parallelism of the Reduce operation, the master node schedules the Reduce operations on the same node as much as possible, or on the node closest to the operational data. This is a coarse-grained and strong data parallel in Map operations, and is very useful for applications with high-performance parallelism requirements. For the different classifications of disaster-monitoring models presented in the previous section, in the following we present their cloud computing models. This algorithm is mainly for processing in the disaster-monitoring model with pure numerical calculation and no spatial information processing and transformation. In Section 4.1, \(F(x,y)=G[f(x,y)]\) is used to represent the calculation model of numerical calculation. According to its characteristic analysis, numerical calculation does not need to be statistically calculated under normal circumstances. It is not necessary to have Reduce. Each block of data is processed independently. Using the MapReduce model, for specific disaster-monitoring model applications, the specific cloud computing model is as Algorithm 1. ```
1:the serialized remote sensing data. 2:Intermediate results for the next job input. 3:functionMapper(key1, value1)
4:for all item\(\in\)f(x, y)do
5:F(x,y) = G[item];
6:endfor
7:key2 = key1; value2 = serialize(metadata, F(x,y));
8:output(key2, value2);
9:endfunction
```  
**Algorithm 1**The cloud computing model for Numerical Calculation  
(2) Iterative Calculation  
This is mainly used for iterative calculations of equations, matrix eigenvalues, etc. \(\mathrm{F(i+1)}=\mathrm{G[F(i),C]}\) is a computing model of iterative calculation. According to the analysis of the characteristics of the iterative calculation, the iterative computational processing is performed on the MapReduce operation until the condition is met. For specific disaster-monitoring model applications, the specific cloud computing model is as Algorithm 2. ```
1:the serialized remote sensing data. 2:Intermediate results for the next job input.