Crash consistency is a critical challenge that storage systems need to address carefully in multiple layers, including the database, file system, and block-layer RAID. Software approaches typically resort to logging to ensure transactional atomicity, which causes significant performance overhead and write amplification over underlying high-speed SSDs. Motivated by the out-of-place update feature of SSD-internalflash translation layer (FTL), previous studies propose crash-consistent SSDs to offload transactional atomicity guarantee and demonstrate their effectiveness in eliminating software-based logging overheads. However, existing hardware offloading approaches only consider single-SSD systems and would fail in an SSD array. This paper presents a crash-consistent SSD array, using FTLs and coordinating multiple SSDs to provide transactional atomicity across the array. The key is to design anarray-wide transaction commit (ARC)protocol, which resolves the multi-SSD coordination challenge and tolerates disk failures. We implement an ARC array manager and ARC SSDs to verify the design. Two case studies are conducted, where the ARC array is utilized to address the transaction logging overhead in the SQLite database and the stripe write-hole problem in the RAID subsystem. Experimental results demonstrate that the ARC SSD array can improve system performance by 32% to 93% and reduce write amplification by 39% to 49%, on average, compared with software-based logging approaches.

ZNS SSDs are emerging storage devices promising low cost, high performance, and software definability. This paper proposes Balloon-ZNS that enables transparent compression in ZNS SSDs to enhance cost efficiency. ZNS SSDs, unlike traditional SSDs, require data pages to be stored in logical zones and flash blocks with aligned offsets, conflicting with the management of compressed, variable-length pages. Motivated by a key observation that compressibility locality widely exists in data streams, Balloon-ZNS employs a compressibility-adaptive, slot-aligned storage management scheme to address the intractable conflict. Evaluation with RocksDB shows Balloon-ZNS can reap more than 80% of the compression gain while achieving 7.3% lower to 14.4% higher throughput than a vanilla ZNS SSD, on average, when data compressibility is not poor.

SZ is a lossy floating-point data compressor that excels in compression ratio and throughput for high-performance computing (HPC), time series databases, and deep learning applications. However, SZ performs poorly for small chunks and has slow decompression. We pinpoint the Huffman tree in the quantization factor encoder as the bottleneck of SZ. In this paper, we propose ADT-FSE, a new quantization factor encoder for SZ. Based on the Gaussian distribution of quantization factors, we design an adaptive data transcoding (ADT) scheme to map quantization factors to codes for better compressibility, and then use finite state entropy (FSE) to compress the codes. Experiments show that ADT-FSE improves the quantization factor compression ratio, compression and decompression throughput by up to 5×, 2× and 8×, respectively, over the original SZ Huffman encoder. On average, SZ_ADT is over 2× faster than ZFP in decompression. Case studies of the TDengine time series database and HDF5 file store confirm that SZ_ADT significantly boosts user-perceived application performance. In addition, ADT-FSE makes the compression ratio prediction of SZ_ADT easy and accurate, and has the potential to dramatically reduce the area size of SZ hardware implementation.