Abstract
The wide range of services offered by cloud service providers, including data processing, storage and computing resources, makes it difficult to choose the best provider for a particular application. A workable approach is provided by clustering algorithms, which group cloud service providers according to common characteristics including price, reliability, security, and performance. Among all clustering techniques, fuzzy K-means and K-medoids stand out as popular choices. Non-parametric K-means finds cluster representatives called Medoids, while fuzzy K-means soft clustering method places data points into several clusters with different membership levels. Using these algorithms makes it easy to compare and select the cloud service provider best suited for a particular application. The current research examines the application of K-medoids and fuzzy K-means clustering techniques and its performance matrix such as Silhouette Score, Cluster Cohesion, Cluster Separation, Calinski-Harabasz Index(CH), Davies-Bouldin Index(DBI), Inertia and Adjusted Rand Index (ARI) in cloud service provider selection.
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