Cloud Migration Research: A Systematic Review
Background--By leveraging cloud services, organizations can deploy their software systems over a pool of resources. However, organizations heavily depend on their business-critical systems, which have been developed over long periods. These legacy applications are usually deployed on-premise. In rec...
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Published in | IEEE transactions on cloud computing Vol. 1; no. 2; pp. 142 - 157 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Piscataway
IEEE Computer Society
01.07.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | Background--By leveraging cloud services, organizations can deploy their software systems over a pool of resources. However, organizations heavily depend on their business-critical systems, which have been developed over long periods. These legacy applications are usually deployed on-premise. In recent years, research in cloud migration has been carried out. However, there is no secondary study to consolidate this research. Objective--This paper aims to identify, taxonomically classify, and systematically compare existing research on cloud migration. Method--We conducted a systematic literature review (SLR) of 23 selected studies, published from 2010 to 2013. We classified and compared the selected studies based on a characterization framework that we also introduce in this paper. Results--The research synthesis results in a knowledge base of current solutions for legacy-to-cloud migration. This review also identifies research gaps and directions for future research. Conclusion--This review reveals that cloud migration research is still in early stages of maturity, but is advancing. It identifies the needs for a migration framework to help improving the maturity level and consequently trust into cloud migration. This review shows a lack of tool support to automate migration tasks. This study also identifies needs for architectural adaptation and self-adaptive cloud-enabled systems. |
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ISSN: | 2168-7161 2168-7161 2372-0018 |
DOI: | 10.1109/TCC.2013.10 |