# Cloud Computing Based on Computational Characteristics for Disaster Monitoring
## 3 Cloud Computing Model for Disaster Monitoring
### Application of MapReduce for Disaster-Monitoring Model
\begin{tabular}{c} NDWI, AWI, AvgNDWI, NDVI, Calibration correction, \\ Dust extraction, Brightness temperature, Dust intensity, \\ Colorization, Band data extraction \\ \end{tabular}  \\ \hline
2 & Iterative calculation & AOT \\ \hline
3 & Statistical analysis & AvgNDWI, Dust classification, Image segmentation \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Neighbored operation} & \begin{tabular}{c} Mosaic, Colorization, Visualization, Format conversion, \\ Geometric correction, Image segmentation, Neural \\ network classification \\ \end{tabular}  \\ \cline{1-1}  & & \begin{tabular}{c} Geometric correction, Image segmentation, Neural \\ \end{tabular}  \\ \cline{1-1}  & & \begin{tabular}{c} Geometric correction, Image segmentation, Neural \\ \end{tabular}  \\ \cline{1-1}  & & \begin{tabular}{c} Geometric correction, Image segmentation, Neural \\ \end{tabular}  \\ \cline{1-1}  & & \begin{tabular}{c} Geometric correction, Image segmentation, Neural \\ \end{tabular}  \\ \cline{1-1}  & & \ \begin{tabular}{c} Geometric correction, Image segmentation, Neural \\ \end{tabular}  \\ \cline{1-1}  & &
\begin{tabular}{c} Geometric correction, \\ Calibration correction, \\



high requirements on the programmability of the model. The MapReduce implementations offer excellent programmability. This paper adopted SLOC (source lines of code) as a quantitative indicator of programmability. Three typical disaster-monitoring models were experimented and implemented using Java language, and Table 3 shows that programming without the MapReduce model requires hundreds or thousands of lines of additional code. To compare the experimental performance, speedup was selected as the performance comparison index. The speedup defined in Equation (1) refers to the ratio of the total running time in the cloud platform to the total running time in the stand-alone environment (configured as a 4-core CPU). Total running time includes I/O time, calculation time, and system overhead. \[\text{Speedup}=\text{The total running time in the cloud platform/The total running time in} \tag{1}\]

the stand-alone environment

In Figure 2, it shows the speedup of the three kinds of models with the number of computing nodes ranging from 1 to 10. It can be seen that in a cloud computing environment, these models all have good performance, particularly as the number of data nodes increases. Figure 3 shows the experimental results obtained when the amount of remote-sensing data increased from 0.5 GB to about 700 GB. According to the performance curves, the vegetable drought monitoring model implemented on Hadoop exhibited excellent scalability in terms of the data volume. When implemented on a 4-core processor, the runtime metric of this model does not perform very well with increasing data amount. Although the processing power of stand-alone computing cannot be compared with the processing power of cloud computing, it could be concluded from the results, the model with MapReduce shows its excellent scalability with the increasing of data quantity. In addition, we also performed another performance comparison experiment using these three models. These experiments were achieved by increasing the amount of data by 2, 4, and 6 GB. Figure 4

Figure 2: Speed of various models on Hadoop (1 to 10 nodes) with increasing numbers of nodes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Programs (with MR)** & **Programs (without MR)** \\ \hline Vegetable drought monitoring model & 156 & 782 \\ Dust storm monitoring model & 324 & 2754 \\ AOT inversion model & 219 & 2373 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SLOC of three typical disaster-monitoring models. shows the processing time of the different disaster-monitoring models due to increasing the amount of image data on the cloud computing environment. According to the experimental results in Figure 4, as the amount of data increased from 2 to 6 GB, the throughput of the different models was stable and slightly improved.