Enhancing the Performance of Next-Generation SSD Arrays: A Holistic Approach

All-flash array (AFA) is a popular approach to aggregate the capacity of multiple solid-state drives (SSDs) while guaranteeing fault tolerance. Unfortunately, existing AFA engines inflict substantial software overheads on the I/O path, such as the user-kernel context switches and AFA internal tasks (e.g., parity preparation), thereby failing to adopt next-generation high-performance SSDs. Tackling this challenge, we propose ScalaAFA, a unique holistic design of AFA engine that can extend the throughput of next-generation SSD arrays in scale with low CPU costs. We incorporate ScalaAFA into user space to avoid user-kernel context switches while harnessing SSD built-in resources for handling AFA internal tasks. Specifically, in adherence to the lock-free principle of existing user-space storage framework, ScalaAFA substitutes the traditional locks with an efficient message-passing-based permission management scheme to facilitate inter-thread synchronization. Considering the CPU burden imposed by background I/O and parity computation, ScalaAFA proposes to offload these tasks to SSDs. To mitigate host-SSD communication overheads in offloading, ScalaAFA takes a novel data placement policy that enables transparent data gathering and in-situ parity computation. ScalaAFA also addresses the write amplification and metadata persistence issues and avoids GC-caused latency spikes, by thoroughly exploiting SSD architectural innovations. Comprehensive evaluation results indicate that ScalaAFA can achieve 2.5 × write throughput and reduce average write latency by a significant 52.7%, compared to the state-of-the-art AFA engines.

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Publication Details

Journal
ACM Transactions on Storage
Published
2025-05-20
DOI
https://doi.org/10.1145/3736588
Citations
2
Primary Topic
Advanced Data Storage Technologies
Type
article
Field-Weighted Citation Impact
2.97
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article

Enhancing the Performance of Next-Generation SSD Arrays: A Holistic Approach

Bo Mao, Yiming Xu, Xiurui Pan, Shushu Yi et al.
2 citations
ACM Transactions on Storage
Advanced Data Storage Technologies
2.97
article

Enhancing the Performance of Next-Generation SSD Arrays: A Holistic Approach

Bo Mao, Yiming Xu, Xiurui Pan, Shushu Yi, Myoungsoo Jung, Chenxi Wang, Qiao Li, Qiang Li, Jie Zhang
article en
2 citations

Abstract

All-flash array (AFA) is a popular approach to aggregate the capacity of multiple solid-state drives (SSDs) while guaranteeing fault tolerance. Unfortunately, existing AFA engines inflict substantial software overheads on the I/O path, such as the user-kernel context switches and AFA internal tasks (e.g., parity preparation), thereby failing to adopt next-generation high-performance SSDs. Tackling this challenge, we propose ScalaAFA, a unique holistic design of AFA engine that can extend the throughput of next-generation SSD arrays in scale with low CPU costs. We incorporate ScalaAFA into user space to avoid user-kernel context switches while harnessing SSD built-in resources for handling AFA internal tasks. Specifically, in adherence to the lock-free principle of existing user-space storage framework, ScalaAFA substitutes the traditional locks with an efficient message-passing-based permission management scheme to facilitate inter-thread synchronization. Considering the CPU burden imposed by background I/O and parity computation, ScalaAFA proposes to offload these tasks to SSDs. To mitigate host-SSD communication overheads in offloading, ScalaAFA takes a novel data placement policy that enables transparent data gathering and in-situ parity computation. ScalaAFA also addresses the write amplification and metadata persistence issues and avoids GC-caused latency spikes, by thoroughly exploiting SSD architectural innovations. Comprehensive evaluation results indicate that ScalaAFA can achieve 2.5 × write throughput and reduce average write latency by a significant 52.7%, compared to the state-of-the-art AFA engines.

ACM Transactions on Storage
Korea Advanced Institute of Science and Technology (KR), City University of Hong Kong (HK), Xiamen University (CN), Peking University (CN), University of Chinese Academy of Sciences (CN), Alibaba Group (China) (CN)
Openalex Percentile: Top 9%
Advanced Data Storage Technologies
2.97
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