SPDK, as one of the most efficient I/O storage software, is capable of delivering the lowest I/O latency. Unfortunately, the polling mechanism in SPDK wastes tremendous CPU clock cycles, especially under small I/O operations and low queue depths. Although SPDK supports the conventional interrupt method, it does not improve power efficiency under such circumstances. To address this issue, we propose SPDK+, which enables the user interrupt feature in the SPDK to achieve both low latency and high power efficiency. Specifically, SPDK+ employs user interrupt handling to directly process MSI-X interrupts from SSD devices and utilizes user wait instructions during IO wait periods to conserve power. The comprehensive evaluation results show that SPDK+ achieves up to 49.5% power efficiency improvement while keeping the I/O latency almost unchanged compared with SPDK.

The occasional nature of I/O bursts in production clusters makes the substantial and expensive SSD internal hardware resources (e.g., computation and memory resources) always underutilized, resulting in cost inefficiency.Open-Channel SSD (OCSSD), as a pioneering solution, removes the SSD internal resources but rather leverages the host-side resources to serve I/O requests.Unfortunately, it faces adoption obstacles due to the heavy resource contention with user applications, hampered host-SSD collaboration, and proprietary firmware leakage risks.Tackling these challenges, we propose XHarvest, a new cost-efficient and high-performance SSD architecture, which harnesses compute express link (CXL) and trusted execution environment (TEE) to facilitate dynamic, efficient, and secure host resource harvesting.It reserves moderate SSD internal resources to isolate SSD internal tasks and applications under regular I/O loads while coping with occasional I/O bursts via dynamic host resource harvesting.To this end, XHarvest executes the firmware within the host-side TEE without disclosing sensitive

Racetrack memory (RM) techniques have become promising solutions to resolve the memory wall issue as they increase memory density, reduce energy consumption and are capable of building processing-in-memory (PIM) architectures. RM can place arithmetic logic units in or near its memory arrays to process tasks offloaded by the host. While there already exist multiple studies of processing in RM, these solutions, unfortunately, suffer from data transfer overheads imposed by the loose coupling of the memory core and the computation units. To address this issue, we propose StreamPIM, a new processing-in-RM architecture, which tightly couples the memory core and the computation units. Specifically, StreamPIM directly constructs a matrix processor from domain-wall nanowires without the usage of CMOS-based computation units. It also designs a domainwall nanowire-based bus, which can eliminate electromagnetic conversion. StreamPIM further optimizes the performance by leveraging RM internal parallelism. Our evaluation results show that StreamPIM achieves 39.1 × higher performance and saves 58.4 × energy consumption, compared with the traditional computing platform.

All-flash array (AFA) has become one of the most popular storage forms in diverse computing domains. While traditional AFA implementations adopt the block interface to seamlessly integrate with most existing software, this interface hinders the host from managing SSD internal tasks explicitly, which results in both short endurance and poor performance. In comparison, ZNS AFA, such as RAIZN, adopts ZNS SSDs and exposes the ZNS interface to the users. This solution attempts to raise the level of responsibility for SSD management. Unfortunately, it faces severe compatibility issues as most upper-layer software only takes block I/O accesses for granted.

RAID has been widely adopted to enhance the performance, capacity, and reliability of the existing storage systems. However, we observe that the Linux software RAID (mdraid) suffers from its poor implementation of the lock mechanism. To address this, we propose ScalaRAID, which refines the role domain of locks and designs a new data structure to prevent different threads from preempting the RAID resources. By doing so, ScalaRAID can maximize the thread-level parallelism and reduce the time consumption of I/O request handling. Our evaluation results reveal that ScalaRAID can improve throughput by 89.4% while decreasing 99.99th percentile latency by 85.4% compared to mdraid.