JACIII Vol.22 No.3 pp. 295-305
doi: 10.20965/jaciii.2018.p0295


Clustering-Based Cloud Migration Strategies

Mubeen Aslam, Lukman bin AB Rahim, Junzo Watada, and Manzoor Hashmani

Department of Computer and Information Sciences, Universiti Teknologi PETRONAS
32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia

January 19, 2018
February 8, 2018
May 20, 2018
cloud migration, legacy application, migration strategies, migration strategy selection process

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.

Cite this article as:
M. Aslam, L. bin AB Rahim, J. Watada, and M. Hashmani, “Clustering-Based Cloud Migration Strategies,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.3, pp. 295-305, 2018.
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