The k-means algorithm of the partitioning clustering method is used to analyze cloud migration strategies in this study. The extent of assistance required to be provided to organizations while working on migration strategies was investigated for each cloud service model and concrete clusters were formed. This investigation is intended to aid cloud consumers in selecting their required cloud migration strategy. It is not easy for businessmen to select the most appropriate cloud migration strategy, and therefore, we proposed a suitable model to solve this problem. This model comprises a web of migration strategies, which provides an unambiguous visualization of the selected migration strategy. The cloud migration strategy targets the technical aspects linked with cloud facilities and measures the critical realization factors for cloud acceptance. Based on similar features, a correlation among the migration strategies is suggested, and three main clusters are formed accordingly. This helps to link the cloud migration strategies across the cloud service models (software as a service, platform as a service, and infrastructure as a service). This correlation was justified using the digital logic approach. This study is useful for the academia and industry as the proposed migration strategy selection process aids cloud consumers in efficiently selecting a cloud migration strategy for their legacy applications.
Understanding the threshold value classification from various vegetation types may help distinguish spectral reflectance differences in detailed land use studies. However, conducting all of the processes requires relatively large resources regarding manual computation, which could be surpassed by cloud computing. Unfortunately, in Bogor Regency, there is still a lack of research that studies the threshold value of various vegetation types related to forestry and plantation sectors. Land use categories were classified, and threshold values were determined, especially for selected vegetation types including teak, oil palm, rubber, pine, bamboo, and tea based on several vegetation indices in Bogor Regency using the Cloud-Computing platform. The data source was retrieved from 10-meters Sentinel-2 Satellite median imagery of January 2019 - June 2021. Land use maps were generated using Random Forest Algorithm from composite images. Meanwhile, the threshold value of each vegetation type was calculated from the average and standard deviation of NDVI, SAVI, EVI, ARVI, SLAVI, and GNDVI index. The result of the study showed forest and plantation area covers about 158,168.13 ha or 48.92 % of the study area. NDVI was found suitable to identify teak, SLAVI for rubber and pine, EVI for bamboo and tea, and GNDVI for oil palm vegetation.
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