Ellison, Martyn, Calinescu, Radu orcid.org/0000-0002-2678-9260 and Paige, Richard F. orcid.org/0000-0002-1978-9852 (2018) Evaluating cloud database migration options using workload models. Journal of Cloud Computing: Advances, Systems and Applications (JoCCASA). 6. ISSN 2192-113X
Abstract
A key challenge in porting enterprise software systems to the cloud is the migration of their database. Choosing a cloud provider and service option (e.g., a database-as-a-service or a manually configured set of virtual machines) typically requires the estimation of the cost and migration duration for each considered option. Many organisations also require this information for budgeting and planning purposes. Existing cloud migration research focuses on the software components, and therefore does not address this need. We introduce a two-stage approach which accurately estimates the migration cost, migration duration and cloud running costs of relational databases. The first stage of our approach obtains workload and structure models of the database to be migrated from database logs and the database schema. The second stage performs a discrete-event simulation using these models to obtain the cost and duration estimates. We implemented software tools that automate both stages of our approach. An extensive evaluation compares the estimates from our approach against results from real-world cloud database migrations.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s). 2018 |
Keywords: | Cloud migration,Database modelling,Enterprise systems,Model-driven engineering |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 16 Apr 2018 15:40 |
Last Modified: | 11 Jan 2025 00:05 |
Published Version: | https://doi.org/10.1186/s13677-018-0108-5 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1186/s13677-018-0108-5 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129718 |