Solid-state drives (SSDs) that do not have internal dynamic random-access memory (DRAM) are being widely spread for client SSD and embedded SSD markets in recent years because they are cheap and consume less power. Obviously, their performance is lower than conventional SSDs because they cannot exploit advantages of DRAM in the controller. However, this problem can be alleviated by using host memory buffer (HMB) feature of Non-Volatile Memory Express (NVMe), which allows SSDs to utilize the DRAM of host. In this paper, we show that commercial DRAM-less SSDs clearly exhibit worse I/O performance than SSDs with internal DRAM, but this can be improved by using the HMB feature. We also present methods that reveal how the host memory buffer is used in commercial DRAM-less SSDs to improve I/O performance. Through extensive experiments, we conclude that DRAM-less SSDs evaluated in this study mainly exploit the host memory buffer as an address mapping table cache rather than a read cache or write buffer to improve I/O performance.

Hospitals that store sensitive patient medical records have recently faced issues such as the inability to recover medical data and breaches of patient privacy due to hacker attacks. These attacks on medical data often involve ransomware, which obfuscates the entire hospital’s data, making them inaccessible, and can also occur when hospitals share patient information during transfers of care. In this study, we propose a new authentication protocol to prevent and address such issues within hospital systems. The proposed protocol encrypts medical records on a private blockchain, allowing them to be securely shared among institutions, hospitals, and insurance companies, ensuring data recovery even if a ransomware attack paralyzes the server. Additionally, the protocol facilitates the systematic sharing of patient medical records between hospitals or between hospitals and insurance companies by distributing session keys. In this study, we demonstrate that the proposed protocol provides 11 security properties, including forward and backward secrecy, user untraceability, and resistance to replay attacks. We also evaluate the communication and computational costs, proving that the protocol is feasible for practical use.

When processing I/O requests, the current Linux kernel does not adequately consider the urgency of user-centric tasks closely related to user experience. To solve this critical problem, we developed a practical method in this study to enhance user experience in a computing environment wherein non-volatile memory express (NVMe) solid-state drives (SSDs) serve as storage devices. In our proposed scheme, I/O requests that originate from the user-centric tasks were preferentially served across various levels of queues by modifying the multi-queue block I/O layer of the Linux kernel, considering the dispatch method of NVMe SSDs. Our scheme tries to give as fast a path as possible for I/O requests from user-centric tasks among many queues with different levels. Especially, when the SSD is overburdened, it avoids the queues with many pending I/O requests and thus can significantly reduce the I/O latency of user-centric tasks. We implemented our proposed scheme in the Linux kernel and performed practical evaluations on a commercial SSD. The experimental results showed that the proposed scheme achieved significant enhancement in the launch time of five widely used applications by up to ~65%.

Solid-State Drives (SSDs) have become a foundational component of modern storage infrastructures, offering superior latency, throughput, and energy efficiency compared to traditional spinning disks. However, as computing systems continue to scale in complexity and workload diversity, SSDs face mounting challenges in maintaining consistent performance, endurance, and resource utilization. Traditional rule-based management techniques, while efficient under static conditions, struggle to adapt to dynamic I/O patterns. This limitation leads to inefficiencies in core operations such as address translation, garbage collection, wear leveling, and internal scheduling. In this survey, we examine the growing body of research that leverages machine learning (ML) for SSD-internal optimization. Although this area holds significant potential, it has received comparatively less attention than host- or system-level approaches. We present a systematic taxonomy of over 40 representative studies and categorize them based on optimization targets, ML methodologies, evaluation strategies, and implementation maturity.We also highlight critical challenges including computational overhead, dataset limitations, and real-time adaptability, all of which impact the feasibility of deploying ML within resource-constrained SSD controllers. Beyond synthesizing the current state of the art, we identify key trends such as the adoption of deep and reinforcement learning, the emergence of host-assisted learning architectures, and increasing efforts toward prototype-level implementation. Furthermore, we outline future research directions that emphasize holistic optimization across SSD subsystems, cross-layer collaboration, and energy-aware intelligent control. By focusing on the architectural integration of ML directly within SSDs, this survey aims to guide the development of next-generation storage systems that are not only faster and more durable but also inherently self-optimizing and context-aware.