发表机构
Macronix Inc.; IBM Research Zurich; National Taiwan University; National Tsing Hua University; National Cheng Kung University（旺宏电子; IBM 苏黎世研究实验室; 国立台湾大学; 国立清华大学; 国立成功大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对异构SSD集成难题，提出DT-RAID分层RAID架构，通过条带级热度跟踪实现热数据动态放置，性能提升6.8倍，寿命延长20.9倍。
AI 中文摘要
云和人工智能驱动的工作负载的迅速激增，导致现代存储子系统面临日益复杂的需求。为了满足这些需求，固态硬盘（SSD）控制器架构已演变成一个碎片化的格局，提供了针对耐用性、性能或容量进行优化的多层级驱动器类型。最近，SSD开始在同一设备内区分不同区域，实现了驱动器内部异构性。然而，将这种异构性以最小干扰的方式集成到现有存储栈中仍然具有挑战性。在本文中，我们认为存储中间件（如RAID）是应对这些集成挑战的有效控制层。我们提出了DT-RAID，一种面向新兴SSD的驱动器内部异构性感知RAID架构。DT-RAID监控条带级I/O访问模式，并做出在线放置决策，无需修改应用程序。它采用一种轻量级热度跟踪机制，将频繁访问（热）条带动态放置到更高性能、更高耐用性的层级上。基于SNIA MSR企业I/O轨迹的模拟，我们证明，与统一RAID部署相比，DT-RAID在更大的层级不对称性下将建模的I/O性能提升高达6.8倍，并将标准化寿命延长高达20.9倍。
英文摘要
The rapid proliferation of cloud and AI-driven workloads has led to increasingly complex requirements for modern storage subsystems. To meet these demands, SSD controller architectures have evolved into a fragmented landscape, offering tiers of drive types optimized for endurance, performance, or capacity. More recently, SSDs have begun to differentiate regions within the same device, enabling intra-drive heterogeneity. However, integrating such heterogeneity into the existing storage stack with minimal disruption remains challenging. In this paper, we argue that storage middleware, such as RAID, is an effective control layer to address these integration challenges. We present DT-RAID, an intra-drive heterogeneity-aware RAID architecture designed for emerging SSDs. DT-RAID monitors stripe-level I/O access patterns and makes online placement decisions without requiring application modifications. It employs a lightweight heat-tracking mechanism to dynamically place frequently accessed (hot) stripes onto the higher-performance, higher-endurance tier. Using simulations based on SNIA MSR enterprise I/O traces, we demonstrate that DT-RAID improves modeled I/O performance by up to $6.8\times$ under greater tier asymmetry and extends normalized lifespan by up to $20.9\times$ compared to uniform RAID deployments.
Comments12 Pages, 12 Figures
