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Elasticity - where systems acquire and release resources in response to dynamic workloads, while paying only for what they need - is a driving property of cloud computing. At the core of any elastic system is an automated controller. This paper addresses elastic control for multi-tier application services that allocate and release resources in discrete units, such as virtual server instances of predetermined sizes. It focuses on elastic control of the storage tier, in which adding or removing a storage node or "brick" requires rebalancing stored data across the nodes. The storage tier presents new challenges for elastic control: actuator delays (lag) due to rebalancing, interference with applications and sensor measurements, and the need to synchronize the multiple control elements, including rebalancing. We have designed and implemented a new controller for elastic storage systems to address these challenges. Using a popular distributed storage system - the Hadoop Distributed File System (HDFS) - under dynamic Web 2.0 workloads, we show how the controller adapts to workload changes to maintain performance objectives efficiently in a pay-as-you-go cloud computing environment.