The limitations of these existing cloud migration methodologies are described further in “ Related work” section. Similarly, cloud deployment simulators like CDOSim focus only on compute resources. For instance, the ARTIST and REMICS cloud migration methodologies refer to the database but do not support any database specific challenges.
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However, the existing cloud migration work focuses on the software components and gives minimal consideration to data. In either case, the most challenging component to migrate is often the database due to the size and importance of the data it contains. Some organisations have been using clouds for over a decade and are considering switching provider, while others are planning an initial migration. The benefits of hosting an enterprise system on the cloud - instead of on-premise physical servers - are well understood and documented. An extensive evaluation compares the estimates from our approach against results from real-world cloud database migrations. We implemented software tools that automate both stages of our approach. The second stage performs a discrete-event simulation using these models to obtain the cost and duration estimates. The first stage of our approach obtains workload and structure models of the database to be migrated from database logs and the database schema. We introduce a two-stage approach which accurately estimates the migration cost, migration duration and cloud running costs of relational databases. Existing cloud migration research focuses on the software components, and therefore does not address this need. Many organisations also require this information for budgeting and planning purposes.
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. A key challenge in porting enterprise software systems to the cloud is the migration of their database.
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