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.
Authors
- Bo Mao (ORCID: https://orcid.org/0000-0002-4819-4583)
- Yiming Xu
- Xiurui Pan (ORCID: https://orcid.org/0000-0002-2528-2660)
- Shushu Yi (ORCID: https://orcid.org/0000-0002-9061-924X)
- Myoungsoo Jung (ORCID: https://orcid.org/0000-0002-9832-5801)
- Chenxi Wang (ORCID: https://orcid.org/0000-0002-1451-3101)
- Qiao Li (ORCID: https://orcid.org/0000-0002-4579-4268)
- Qiang Li
- Jie Zhang (ORCID: https://orcid.org/0000-0002-6299-4683)
Institutions
- 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)
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