# Cloud Computing Based on Computational Characteristics for Disaster Monitoring
## 3 Cloud Computing Model for Disaster Monitoring
### Application of MapReduce for Disaster-Monitoring Model
The processing of disaster-monitoring models using cloud computing achieved excellent throughput and considerable scalability. Benefiting from the elastic resources provided by the cloud, by expanding the cluster size, the system can maintain performance levels under increased data volumes. Each disaster-monitoring model has the option to work with resources that are appropriate for its computing needs. Each of the three models of the experiment achieved excellent performance improvements in the cloud environment, although algorithms such as iterative calculations did not reach the desired performance. The main reason may be the high computational complexity of these algorithms, i.e., the calculation amount of each task on a single machine is too large to make full use of the substantial computing power of the cloud computing architecture. Overall, most of the algorithms proposed in this paper implemented using cloud computing models achieved promising performance improvements. From the results, it can be concluded that the various algorithms implemented using the proposed cloud computing model show excellent scalability. In addition, the increase in throughput was clearly apparent, and the more data-intensive the computing, the more obvious the increase in throughput. Therefore, the cloud computing model proposed in this paper can be used in typical disaster-monitoring applications and has good performance in terms of processing time and throughput. We conclude

Figure 4: Processing times of various models on Hadoop with 10 nodes as the amount of data increases from 2 to 6 GB. Figure 3: Runtime required to obtain the vegetable drought monitoring products versus stand-alone (4 cores) with increasing data volumes. that the proposed cloud computing models for remote-sensing image models demonstrated their programmability and are suitable for most data-intensive disaster-monitoring algorithms. ## 5 Conclusions

As a new form of Internet technology service for disaster-monitoring applications, cloud computing is a highly effective method for processing data-intensive computing. Based on computational feature analysis, this paper analyzed and classified disaster-monitoring algorithms, and selected the MapReduce model to process the disaster-monitoring algorithm in the current general cloud computing model. The approach taken in this paper provides users with a simple and fast high-level abstraction of cloud computing description method, and allows users to implement the distributed design of disaster-monitoring application models and solve problems encountered in modeling, such as difficulties in programming development and code reuse. This study implemented typical disaster-monitoring application examples based on the prototype system. This approach can be conveniently adopted by users, simplify the description and development process of disaster-monitoring applications, and verify the correctness and ease of use of the proposed method. Experiments have shown that implementing disaster-monitoring applications with MapReduce and running them into the cloud is a good solution in terms of performance and programmability. In the future, more disaster-monitoring models will be tested to verify that cloud computing is the best choice, especially for real-time processing requirements. Conceptualization, Q.Z. and G.L. ; Methodology, Q.Z. and W.Y. ; Software, Q.Z. and W.Y. ; Validation, G.L. ; Formal Analysis, Q.Z. and W.Y. ; Investigation, Q.Z. ; Resources, Q.Z. and W.Y. ; Data Curation, W.Y. and Q.Z. ; Writing--Original Draft Preparation, Q.Z. ; Writing--Review & Editing, G.L. and W.Y. ; Visualization, Q.Z. ; Supervision, G.L. and W.Y. ; Project Administration, Q.Z. and G.L. ; Funding Acquisition, W.Y. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Key Research and Development Program of China from Ministry of Science and Technology (MOST) under Grant 2016 YFB0501504. The authors declare no conflict of interest.