Traditional RAID solutions (e.g., Linux MD) balance writes evenly across the array for high I/O parallelism and data reliability. This is built around the assumption that the underlying storage components are homogeneous, both in performance and capacity. However, SSDs, even for the same model, exhibit very different characteristics and degrade over time, leading to severe disk under-utilization.

We argue that wear leveling in SSDs does more harm than good under modern settings where the endurance limit is in the hundreds. To support this claim, we evaluate existing wear leveling techniques and show that they exhibit anomalous behaviors and produce a high write amplification. These findings are consistent with a recent large-scale field study on the operational characteristics of SSDs. We discuss the option of forgoing wear leveling and instead adopting capacity variance in SSDs, and show that the capacity variance extends the lifetime of the SSD by up to 2.94×.

We present FF-SSD, a machine learning-based SSD aging framework that generates representative future wear-out states. FF-SSD is accurate (up to 99% similarity), efficient (accelerates simulation time by 2×), and modular (can be integrated with existing simulators and emulators).