Automated storage management is critical for most data-intensive applications running on DBMSs. In large-scale storage subsystems, the workload is expected to vary with time. In order to ensure both QoS and efficient usage of storage resources, variation in the actual physical disks is allowed to support a single virtual disk. Such data migration generates extra IOs and consumes storage resources. Not only does data migration need to be scheduled ahead but it must also be scheduled in such a way that QoS violations do not occur because of the extra migration IOs. In this paper, we present a novel analytic framework, PUL-STORE, for autonomically managing the storage to provide performance guarantee during migration.