Autonomic Resource Management for Virtualized Database Hosting Systems

The hosting of databases on virtual machines (VMs) has great potential to improve the efficiency of resource utilization and the ease of deployment of database systems. This paper considers the problem of allocation of physical resources on demand to a database’s VM according to QoS (Quality of Service) requirements. This is a challenging problem because of the highly dynamic and complex nature of database systems and their workloads. An autonomic resource management approach is proposed to address this problem based on adaptive fuzzy modeling and prediction techniques. The approach can effectively capture the relationship between a dynamically changing database workload, which is both CPU and I/O intensive, and its VM’s consumption of resources, including both CPU cycles and disk bandwidth. It can be used to predict the resource needs of a database VM online and to guide the on-demand resource allocation according to the workload demand and desired QoS. A prototype of the proposed resource management system is evaluated using typical database workloads based on TPC-H and RUBiS. The results demonstrate that the proposed approach can efficiently allocate resources for a database VM that is serving CPU and I/O intensive queries while meeting the QoS targets.