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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. In this work, we present Asymmetric-RAID (Asym-RAID), a novel RAID architecture that optimizes system performance and storage utilization by exploiting heterogeneity from a larger SSD pool. Asym-RAID asymmetrically distributes data across the array to fully utilize the capacity of each SSD. To improve performance, Asym-RAID differentially exports the address space of each data stripe to the host, allowing for performance-optimized data placement. We outline the necessary changes in the storage stack for building an asymmetric RAID system and highlight its benefits. 

We present the design and implementation of a capacityvariant storage system (CVSS) for flash-based solid-state drives (SSDs). CVSS aims to maintain high performance throughout the lifetime of an SSD by allowing storage capacity to gracefully reduce over time, thus preventing fail-slow symptoms. The CVSS comprises three key components: (1) CV-SSD, an SSD that minimizes write amplification and gracefully reduces its exported capacity with age; (2) CV-FS, a log-structured file system for elastic logical partition; and (3) CV-manager, a user-level program that orchestrates system components based on the state of the storage system. We demonstrate the effectiveness of CVSS with synthetic and real workloads, and show its significant improvements in latency, throughput, and lifetime compared to a fixed-capacity storage system. Specifically, under real workloads, CVSS reduces the latency, improves the throughput, and extends the lifetime by 8–53%, 49–316%, and 268–327%, respectively. 

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 IOTap, a tool that analyzes and profiles block I/O traces. IOTap computes the (dis)similarities among a set of workloads and sets a guideline for selecting a subset of traces for benchmarking. By doing so, we avoid experimentally running all workloads or, even worse, arbitrarily selecting a subset that skews the results.We demonstrate the usefulness of IOTap by comparing its results with experiments on real SSDs, achieving a high correlation of 0.92 for an NVMe SSD. 

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).