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Creators/Authors contains: "Kim, Bryan S."

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  1. Modern SSDs achieve high throughput by utilizing multiple independent channels and chips in parallel. However, we find that excessive parallelism inadvertently amplifies the garbage collection (GC) overhead due to the larger unit of space reclamation. Based on this observation, we design PLAN, a novel SSD parallelism management and data placement scheme that allocates different levels of parallelism to different workloads with different needs to minimize the GC overhead. We demonstrate the effectiveness of PLAN by evaluating it against other state-of-the-art designs across various real-world workloads. PLAN reduces write amplification with comparable or better performance to the other designs that are always at full parallelism. 
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    Free, publicly-accessible full text available July 9, 2024
  2. 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). 
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  3. 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×. 
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  4. 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. 
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