# Cloud Computing Based on Computational Characteristics for Disaster Monitoring
## 2 Related Work
### Cloud Computing for Remote-Sensing Data Analysis  
The object of cloud computing is computing on a distributed cluster and providing the results to the client under time requirements. The advantages of cloud computing are support for distributed computing and distributed storage, as well as support for heterogeneity and ease of programming. With the emergence and development of cloud computing technology, an increasing number of scientists are using cloud computing to process and analyze large-scale remote-sensing images and geoscience data, and have proposed many ideas relating to geoscience data processing using cloud architecture. Numerous studies of analysis and processing have been conducted. Most research experiments have used Microsoft Azure, Amazon's EC2, open source platforms such as Hadoop and Openstack, or other mature cloud platforms. These platforms provide cloud services to support spatial information modeling, providing the interface of SaaS (software-as-a-Service), PaaS (Platform-as-a-Service), or IaaS (Infrastructure-as-a-Service) to climate researchers who may be unfamiliar with the underlying architecture. The purpose of cloud computing is to serve the user more easily. Its programming model is also simplified to ensure that the execution and scheduling of complex parallel tasks in the background are transparent to users and programmers. Users can easily write programs to achieve their application goals, thus making it easier to take advantage of cloud computing services using the programming model of cloud computing. The MapReduce programming model designed by Google is a very popular cloud computing model due to its simple programming requirements. Several mature cloud platforms, such as Hadoop and Microsoft Azure, are currently based on the MapReduce programming framework. As a result, distributed data research in many disciplines commonly uses MapReduce [27; 28; 29]. Data locality is a key factor in task scheduling performance in MapReduce, and has been addressed in the literature by increasing the number of local processing tasks [30]. All internal processes are transparent for developers, enabling ease of programming. The message passing interface (MPI) is also commonly used in parallel computing. For MPI, the data needs to be passed on as a message for computation; in MapReduce, the calculation function is moved to the data to minimize, if not eliminate, data movement issues. Recent studies have proposed a novel partitioner [31], a simultaneous scheduling of Map and Reduce tasks [32], and a distributed heuristic scheduling algorithm [33] to improve data locality and increase the performance of MapReduce applications. The large amount of remote-sensing data received from satellites poses a substantial challenge for remote-sensing application scientists to effectively manage and analyze big data. To perform query analysis on remote-sensing data, scientists first decrypt the standard format of the original satellite data and localize the distributed preprocessing steps on the equal split of datasets in Hadoop [17; 34]. They then use cloud computing technology to efficiently query the data. [PERSON] et al. proposed ClimateSpark, a distributed computing framework in memory, which uses a block data structure to efficiently query different spatiotemporal data [16]. [PERSON] et al. used Hive to query terabytes of climate data, in which query was processed as a MapReduce task, which showed good flexibility and performance [35]. To perform additional high-performance query analysis, scientists have researched and devised methods based on spatial coding, such as Hibert curves, to index image data and use it to divide large spatial data in Hadoop with the aim of improving query performance [36; 37]. Remote-sensing images are increasingly used in the development of Earth observation satellites to investigate human activities, monitor environmental changes, and update existing geospatial data.