A log-structured merge-tree-based key value store (LSMKV) is an append-only database for storing and retrieving unstructured data, especially in a write-intensive environment. This database uses hierarchical components to store and manage data. Upper-level components have a shorter data lifespan and a higher access locality than lower-level components. Hence, the data access latency of the upper-level components significantly affects the performance of the entire database. Hybrid solid-state drives (SSD) composed of media with different access speeds can improve the performance of an LSMKV by storing the upper-level components using a fast storage space. However, many hybrid SSDs use fast storage spaces to store data that are frequently allocated to the same logical address; they are not suitable for storing appendonly component data, which are allocated to adjacent logical addresses. This paper proposes a hybrid SSDmanagement method to reduce the data access latency of append-only LSMKVs and increase the durability of hybrid SSDs. The proposed method allocates the data of upper-level components to a fast storage space using the level information of the data as a hint. This study utilizes dynamic data separation to determine the components to be placed in the fast storage space, NAND block management to store the data with similar lifespans in the same fast NAND block, and a data-relocation method to migrate long-lived data from the fast NAND region to another NAND region. Experimental results indicate that the proposed method reduces the average I/O latency by an average of 12% and increases the device durability by an average of 22%.

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As big data has evolved over the past few years, a lack of storage space and I/O bandwidth has become one of the most important challenges to overcome. To mitigate these problems, data compression schemes reduce the amount of data to be stored and transmitted at the cost of additional CPU overhead. Many researchers have attempted to reduce the computational load imposed on the CPU by data compression using specialized hardware. However, space savings through data compression often comes from only a small portion of data. Therefore, compressing all data, regardless of data compressibility, can waste computational resources. Our work aims to decrease the cost of data compression by introducing a selective data compression scheme based on data compressibility prediction. The proposed compressibility prediction method provides more fine-grained selectivity for combinational compression. Additionally, our method reduces the amount of resources consumed by the compressibility predictor, enabling selective compression at a low cost. To verify the proposed scheme, we implemented a DEFLATE compression system on a field-programmable gate array platform. Experimental results demonstrate that the proposed scheme improves compression throughput by 34.15% with a negligible decrease in compression ratio. INDEX TERMS Data compression, Huffman coding, LZ77 encoding, accelerator architecture, field programmable gate array, estimation, compressibility.

Many flash storage systems divide input/output (I/O) requests that require large amounts of data into sub-requests to exploit their internal parallelism. In this case, an I/O request can be completed only after all sub-requests have been completed. Thus, non-critical sub-requests that are completed quickly do not affect I/O latency. To efficiently reduce I/O latency, we propose a buffer management scheme that allocates buffer space by considering the relationship between the processing time of the sub-request and I/O latency. The proposed scheme prevents non-critical sub-requests from wasting ready-to-use buffer space by avoiding the situation in which buffer spaces that are and are not ready to use are allocated to an I/O request. To allocate the same type of buffer space to an I/O request, the proposed scheme first groups sub-requests derived from the same I/O request and then applies a policy for allocating buffer space in units of subrequest groups. When the ready-to-use buffer space is insufficient to be allocated to the sub-request group being processed at a given time, the proposed scheme does not allocate it to the sub-request group but it instead sets it aside for future I/O requests. The results of the experiments to test the proposed scheme show that it can reduce I/O latency by up to 24% compared with prevalent buffer management schemes.

Physically-addressable solid-state drives (PASSDs) are secondary storage devices that provide a physical address-based interface for a host system to directly control NAND flash memory. PASSDs overcome the shortcomings such as latency variability, resource under-utilization, and log-on-log that are associated with legacy SSDs. However, in some operating environments, the write response time significantly increases because the PASSD reports the completion of a host write command synchronously (i.e., write-through) owing to reliability problems. It contrasts asynchronous processing (i.e., write-back), which reports a completion immediately after data are received in a high-performance volatile memory subsequently used as a write buffer to conceal the operation time of NAND flash memory. Herein, we propose a new scheme that guarantees write reliability to enable a reliable asynchronous write operation in PASSD. It is designed to use a large-granularity mapping table for minimizing the memory requirements and performing internal operations at an idle time to avoid response delays. Results demonstrate that the proposed PASSD reduces the average write response time by up to 88% and guarantees reliability without performance degradation.