Storage elasticity on IaaS clouds is a crucial feature in the age of data-intensive computing. However, the traditional provisioning model of leveraging virtual disks of fixed capacity and performance characteristics has limited ability to match the increasingly dynamic nature of I/O application requirements. This mismatch is particularly problematic in the context of scientific applications that interleave periods of I/O inactivity with I/O intensive bursts. In this context, overprovisioning for best performance during peaks leads to significant extra costs because of unnecessarily tied-up resources, while any other trade-off leads to performance loss. This paper provides a transparent solution that automatically boosts I/O bandwidth during peaks for underlying virtual disks, effectively avoiding overprovisioning 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. We demonstrate the benefits of our proposal both for microbenchmarks and for two real-life applications using large-scale experiments.