Prototyping of Utilization Model for KOMPSAT-3/3A Analysis Ready Data Based on the Open Data Cube Platform in Multi-Cloud Computing Environment: A Case Study  
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required resources. This makes it possible to quickly change the system configuration and add the necessary resources to build a system optimized for EO satellite data. Third, it has high security. Cloud service providers are very interested in security and offer various security solutions. In addition, cloud systems provide functions such as data backup, restoration, and disaster recovery, ensuring data stability. Fourth, it is cost-effective. Using a cloud system can reduce costs for purchasing or maintaining resources such as servers or storage. In addition, cloud services adopt a pay-as-go model, meaning that only the necessary resources are used, making cost management more efficient. With these advantageous points of the cloud computing platform, many attempts and achievements have been made in developing remote sensing platforms or frameworks based on cloud computing environments for EO applications. [PERSON] et al. [4] proposed a machine learning approach to predict the computational intensity by dividing the spatial domain into balanced subdivisions for better performance. [PERSON] et al. [5] developed a product generation system using multi-source remote sensing data in cloud computing. [PERSON] et al. [6] described how EO data can be utilized by using cloud computing in discrete global grid systems. [PERSON] et al. [7] used an elastic computing scheme with a Spark engine on Kubernetes for real-time fusion processing of remotely sensed big data. [PERSON] [8] summarized the state of Landsat migration in the cloud environment at the United States Geological Survey (USGS). [PERSON] et al. [9] proposed a scalable EO data processing platform interoperable with high-performance computing (HPC) cloud and EO data repositories, capable of connecting any data repository that supports web coverage service. In addition to the research on applying cloud technology for data management and distribution of satellite imagery as described above, there is also active research being conducted on SaaS that provides practical analysis capabilities in various fields such as land cover mapping, disaster monitoring, and wetland monitoring [10; 11; 12; 13]. As mentioned above, there are various approaches to research on the application of cloud computing for EO data. This study aims to explore research that specifically focuses on integrating topics such as ARD and ODC with cloud computing, which can be operated in OpenStack and AWS environments. This is because ODC is typically operated on AWS, and ODC inherently provides ARD as a fundamental feature. In this study, the OpenStack cloud environment is employed in conjunction with AWS as the cloud environment. ARD enables users to easily access data without complex processing by generating data for areas of interest on a regular basis. Research on the types and generation of ARD in satellite information has consistently received attention as one of the topics of interest. Over the years, efforts have been made to provide ARD for Landsat satellites. [PERSON] et al. [14] defined the contents of Landsat ARD, and the United States Geological Survey (USGS) provided the ARD Data Format Control Book for all Landsat products [15; 16]. Producing data in the form of ARD is an important issue. In addition, it is critical for future applications to explore approaches for distributing data at a similar level. In particular, ARD is processed in large time series rather than individual datasets. Difficulties may arise when providing this through conventional techniques.