Title: Migrating enterprise applications to the cloud: methodology and evaluation

Authors: Steve Strauch; Vasilios Andrikopoulos; Dimka Karastoyanova; Frank Leymann; Nikolay Nachev; Albrecht Stäbler

Addresses: Institute of Architecture of Application Systems, University of Stuttgart, Universitaetsstrasse 38, 70569 Stuttgart, Germany ' Institute of Architecture of Application Systems, University of Stuttgart, Universitaetsstrasse 38, 70569 Stuttgart, Germany ' Institute of Architecture of Application Systems, University of Stuttgart, Universitaetsstrasse 38, 70569 Stuttgart, Germany ' Institute of Architecture of Application Systems, University of Stuttgart, Universitaetsstrasse 38, 70569 Stuttgart, Germany ' NovaTec Holding GmbH Dieselstrasse 18/1 70771 Leinfelden-Echterdingen, Germany ' NovaTec Holding GmbH, Dieselstrasse 18/1, 70771 Leinfelden-Echterdingen, Germany

Abstract: Migrating existing on-premise applications to the cloud is a complex and multi-dimensional task and may require adapting the applications themselves significantly. For example, when considering the migration of the database layer of an application, which provides data persistence and manipulation capabilities, it is necessary to address aspects like differences in the granularity of interactions and data confidentiality, and to enable the interaction of the application with remote data sources. In this work, we present a methodology for application migration to the cloud that takes these aspects into account. In addition, we also introduce a tool for decision support, application refactoring and data migration that assists application developers in realising this methodology. We evaluate the proposed methodology and enabling tool using a case study in collaboration with an IT enterprise.

Keywords: data migration; application migration; decision support; database layer; application refactoring; cloud computing; interaction granularity; data confidentiality; remote data sources.

DOI: 10.1504/IJBDI.2014.066319

International Journal of Big Data Intelligence, 2014 Vol.1 No.3, pp.127 - 140

Received: 30 Nov 2013
Accepted: 12 Dec 2013

Published online: 16 Dec 2014 *

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