Automatic Data Migration into the Cloud

Relational databases have been used for decades to store data. Using scale up, relational databases require a bigger and bigger server with more CPUs, more memory, and more disk storage to keep all the tables to support more concurrent users. However, big servers tend to be highly complex, proprietary, and disproportionately expensive, unlike the low-cost, commodity hardware. Therefore, it becomes important to store data efficiently and compute with massive amount of data, providing high scalability, providing high performance and availability at low costs. This leads to the invention of cloud databases, for instance NoSQL databases. NoSQL databases have many advantages such as reading and writing data quickly, supporting massive storage and low cost. The scaling approach in cloud databases is scale out, which is used to add multiple servers, and the data structure of storage is in the form of key-value pairs. However, it can be a challenge for enterprises to migrate existing relational databases to highly scalable NoSQL databases on clouds. In this thesis, we propose an automatic data migration model which will assist enterprises to migrate their relational databases efficiently and transparently to the cloud databases. We propose four migration methods to migrate data in four different ways. Each migration method is independent of the others and stores the migrated relational database in different formats in the cloud database. We design a system to implement the automatic data migration model. As a proof of concept, we successfully migrated a relational database from Microsoft SQL Server to a cloud database Amazon SimpleDB using four different migration methods. Furthermore, we have conducted extensive experiments on Amazon SimpleDB to evaluate the performance of our model in terms of computational time, storage cost, sharding and redundancy. Based on these experiments and detailed analysis of each migration method, our system allows enterprises to determine which method is suitable for their data migration. Furthermore, our experimental evaluation shows that our solution is promising and can migrate data from the relational databases to the cloud databases