Analysis of a Cloud Migration Framework for Offline Risk Assessment of Cloud Service Providers
Abstract
Generic notions of security on cloud platforms make clients apprehensive about fully migrating their applications on these platforms. The challenge lies in the capability of personalizing the security assessments of different cloud service providers from the perspective of the security requirements of the client applications to be hosted on them. This challenge was addressed by the previously proposed offline risk assessment framework for cloud service providers. This article presents a comprehensive analysis of a cloud migration framework that has been designed by adapting the novel security assessment principles of the offline risk assessment framework. The migration strategy has been modeled as a multiobjective optimization problem to further study the performance of numerous evolutionary algorithms in designing various cloud migration scenarios. The overall effectiveness of the proposed framework has been examined using a use-case application scenario and semisynthetic cloud service providers.
Recommended Citation
A. Sen and S. K. Madria, "Analysis of a Cloud Migration Framework for Offline Risk Assessment of Cloud Service Providers," Software - Practice and Experience, vol. 50, no. 6, pp. 998 - 1021, John Wiley & Sons Ltd, Jun 2020.
The definitive version is available at https://doi.org/10.1002/spe.2809
Department(s)
Computer Science
Research Center/Lab(s)
Center for Research in Energy and Environment (CREE)
Second Research Center/Lab
Center for High Performance Computing Research
Third Research Center/Lab
Intelligent Systems Center
Keywords and Phrases
Cloud Migration; Cloud Security; Genetic Algorithm; Multiobjective Optimization; Risk Assessment; Sensitivity Analysis
International Standard Serial Number (ISSN)
0038-0644; 1097-024X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 John Wiley & Sons Ltd, All rights reserved.
Publication Date
01 Jun 2020
Comments
This research is partially funded by NSF grant IIP-1332002.