Storage elasticity on IaaS clouds is a crucial feature in the age of data-intensive computing, especially when considering fluctuations of I/O throughput. This paper provides a transparent solution that automatically boosts I/O bandwidth during peaks for underlying virtual disks, effectively avoiding over-provisioning without performance loss. Our proposal relies on the idea of leveraging short-lived virtual disks of better performance characteristics (and thus more expensive) to act during peaks as a caching layer for the persistent virtual disks where the application data is stored. We show how this idea can be achieved efficiently at the block-device level, using a caching mechanism that leverages iterative behavior and learns from past experience. Furthermore, we introduce a performance and cost prediction methodology that can be used both independently to estimate in advance what trade-off between performance and cost is possible, as well as an optimization technique that enables better cache size selection to meet the desired performance level with minimal cost. We demonstrate the benefits of our proposal both for microbenchmarks and for two real-life applications using large-scale experiments.