A fragmentation algorithm for storage management in cloud database environment

The past decade has witnessed using cloud DBMSs for enterprises' applications and managing their data. However, only a few number of cloud DBMSs have provided relational database as a service and get benefits from being in cloud data centers. Cloud DBMSs are grouped into two categories. The first category is scalable datastores that cannot preserve ACID for transactions over the entire database. The second one is scalable traditional DBMSs that scale difficulty as they migrate data or instances. This paper proposes a shared storage architecture for cloud DBMS that reduces migration of data that happens for preserving load balance between database instances after increasing scalability by adding new database instances or new storage devices. Moreover, it provides storage management module that locates database objects well on storage devices for parallel access in `write and read' operations with reducing storage skewness and bottleneck, based on a new horizontally fragmentation algorithm. Also, it provides storage monitoring module for detecting skewness and reconfiguring the storage. The proposed Cloud DBMS overcomes limitations in scalable data stores and traditional DBMSs. We finally confirm the effectiveness of the proposed architecture on real data sets.