Clustering-based cloud migration strategies

Aslam, M. and Rahim, L.B.A. and Watada, J. and Hashmani, M. (2018) Clustering-based cloud migration strategies. Journal of Advanced Computational Intelligence and Intelligent Informatics, 22 (3). pp. 295-305.

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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. © 2018 Fuji Technology Press. All Rights Reserved.

Item Type:Article
Impact Factor:cited By 0
Uncontrolled Keywords:Distributed database systems; Infrastructure as a service (IaaS); Platform as a Service (PaaS); Software as a service (SaaS), Cloud consumers; Cloud migrations; Cloud service models; Clustering methods; k-Means algorithm; Legacy applications; Migration strategy; Technical aspects, Clustering algorithms
ID Code:20941
Deposited By: Ahmad Suhairi
Deposited On:26 Feb 2019 02:58
Last Modified:26 Feb 2019 02:58

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