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.
Abstract. Enterprise software systems are prevalent in many organisations, typically they are data-intensive and manage customer, sales, or other important data. When an enterprise system needs to be modernised or migrated (e.g. to the cloud) it is necessary to understand the structure of this data and how it is used. We have developed a toolsupported approach to model database structure, query patterns, and growth patterns. Compared to existing work, our tool offers increased system support and extensibility which is vital for use in industry. Standardisation and platform independence is ensured by producing models conforming to the Knowledge Discovery Metamodel and Software Metrics Metamodel.
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