ACM Transactions on Storage最新文献

{"title":"ReadGuard: Integrated SSD Management for Priority-Aware Read Performance Differentiation","authors":"Myoungjun Chun, Myungsuk Kim, Dusol Lee, Jisung Park, Jihong Kim","doi":"10.1145/3676884","DOIUrl":"https://doi.org/10.1145/3676884","url":null,"abstract":"\u0000 When multiple apps with different I/O priorities share a high-performance SSD, it is important to differentiate the I/O QoS level based on the I/O priority of each app. In this paper, we study how a modern flash-based SSD should be designed to support priority-aware read performance differentiation. From an in-depth evaluation study using 3D TLC SSDs, we observed that existing FTLs have several weaknesses that need to be improved for better read performance differentiation. In order to overcome the existing FTL weaknesses, we propose\u0000 ReadGuard\u0000 , a novel priority-aware SSD management technique that enables an FTL to manage its blocks in a fully read-latency-aware fashion.\u0000 ReadGuard\u0000 leverages a new read-latency-centric block quality marker that can accurately distinguish the read latency of a block and ensures that higher-quality blocks are used for higher-priority apps.\u0000 ReadGuard\u0000 extends an existing suspend/resume technique to handle collisions among reads. Our experimental results show that a\u0000 ReadGuard\u0000 -enabled SSD is effective in supporting differentiated read performance in modern 3D flash SSDs.\u0000","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From SSDs Back to HDDs: Optimizing VDO to Support Inline Deduplication and Compression for HDDs as Primary Storage Media","authors":"Patrick Raaf, André Brinkmann, E. Borba, Hossen Asadi, Sai Narasimhamurthy, John Bent, Mohamad El-Batal, Reza Salkhordeh","doi":"10.1145/3678250","DOIUrl":"https://doi.org/10.1145/3678250","url":null,"abstract":"Deduplication and compression are powerful techniques to reduce the ratio between the quantity of logical data stored and the physical amount of consumed storage. Deduplication can impose significant performance overheads, as duplicate detection for large systems induces random accesses to the backend storage. These random accesses have led to the concern that deduplication for primary storage and HDDs are not compatible. Most inline data reduction solutions are therefore optimized for SSDs and discourage their use for HDDs, even for sequential workloads.\u0000 \u0000 In this work, we show that these concerns are valid if and only if the lessons learned from deduplication research are not applied. We have therefore investigated data reduction solutions for primary storage based on the RedHat\u0000 Virtual Disk Optimizer\u0000 (VDO) and show that directly applying them can decrease sequential write performance for HDDs by 36-times. We then show that slight modifications to VDO plus the integration of a very small SSD area significantly improve performance even beyond the performance without data reduction enabled, making HDDs more cost-efficient for a wide range of mostly sequential Cloud workloads than SSDs. Additionally, these VDO optimizations do not require to maintain different code bases for HDDs and SSDs and we therefore provide the first data reduction solution applicable to both storage media.\u0000","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extremely-Compressed SSDs with I/O Behavior Prediction","authors":"Xiangyu Yao, Qiao Li, Kaihuan Lin, Xinbiao Gan, Jie Zhang, Congming Gao, Zhirong Shen, Quanqing Xu, Chuanhui Yang, Jason Xue","doi":"10.1145/3677044","DOIUrl":"https://doi.org/10.1145/3677044","url":null,"abstract":"As the data volume continues to grow exponentially, there is an increasing demand for large storage system capacity. Data compression techniques effectively reduce the volume of written data, enhancing space efficiency. As a result, many modern SSDs have already incorporated data compression capabilities. However, data compression introduces additional processing overhead in critical I/O paths, potentially affecting system performance. Currently, most compression solutions in flash-based storage systems employ fixed compression algorithms for all incoming data without leveraging differences among various data access patterns. This leads to sub-optimal compression efficiency.\u0000 This paper proposes a data-type-aware Flash Translation Layer (DAFTL) scheme to maximize space efficiency without compromising system performance. First, we propose an I/O behavior prediction method to forecast future access on specific data. Then, DAFTL matches data types with distinct I/O behaviors to compression algorithms of varying intensities, achieving an optimal balance between performance and space efficiency. Specifically, it employs higher-intensity compression algorithms for less frequently accessed data to maximize space efficiency. For frequently accessed data, it utilizes lower-intensity but faster compression algorithms to maintain system performance. Finally, an improved compact compression method is proposed to effectively eliminate page fragmentation and further enhance space efficiency. Extensive evaluations using a variety of real-world workloads, as well as the workloads with real data we collected on our platforms, demonstrate that DAFTL achieves more data reductions than other approaches. When compared to the state-of-the-art compression schemes, DAFTL reduces the total number of pages written to the SSD by an average of 8%, 21.3%, and 25.6% for data with high, medium, and low compressibility, respectively. In the case of workloads with real data, DAFTL achieves an average reduction of 10.4% in the total number of pages written to SSD. Furthermore, DAFTL exhibits comparable or even improved read and write performance compared to other solutions.","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141642190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to the Special Section on USENIX OSDI 2023","authors":"Roxana Geambasu, Ed Nightingale","doi":"10.1145/3654801","DOIUrl":"https://doi.org/10.1145/3654801","url":null,"abstract":"An Efficient Authenticated Storage for Blockchain” by","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LVMT: An Efficient Authenticated Storage for Blockchain","authors":"Chenxing Li, Sidi Mohamed Beillahi, Guang Yang, Ming Wu, Wei Xu, Fan Long","doi":"10.1145/3664818","DOIUrl":"https://doi.org/10.1145/3664818","url":null,"abstract":"<p>Authenticated storage access is the performance bottleneck of a blockchain, because each access can be amplified to potentially <i>O</i>(log <i>n</i>) disk I/O operations in the standard Merkle Patricia Trie (MPT) storage structure. In this paper, we propose a multi-Layer Versioned Multipoint Trie (LVMT), a novel high-performance blockchain storage with significantly reduced I/O amplifications. LVMT uses the authenticated multipoint evaluation tree (AMT) vector commitment protocol to update commitment proofs in constant time. LVMT adopts a multi-layer design to support unlimited key-value pairs and stores version numbers instead of value hashes to avoid costly elliptic curve multiplication operations. In our experiment, LVMT outperforms the MPT in real Ethereum traces, delivering read and write operations six times faster. It also boosts blockchain system execution throughput by up to 2.7 times.</p>","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141059453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Design of Fast Delta Encoding for Delta Compression Based Storage Systems","authors":"Haoliang Tan, Wen Xia, Xiangyu Zou, Cai Deng, Qing Liao, Zhaoquan Gu","doi":"10.1145/3664817","DOIUrl":"https://doi.org/10.1145/3664817","url":null,"abstract":"<p>Delta encoding is a data reduction technique capable of calculating the differences (i.e., delta) among very similar files and chunks. It is widely used for various applications, such as synchronization replication, backup/archival storage, cache compression, etc. However, delta encoding is computationally costly due to its time-consuming word-matching operations for delta calculation. Existing delta encoding approaches either run at a slow encoding speed, such as Xdelta and Zdelta, or at a low compression ratio, such as Ddelta and Edelta. In this paper, we propose Gdelta, a fast delta encoding approach with a high compression ratio. The key idea behind Gdelta is the combined use of five techniques: (1) employing an improved Gear-based rolling hash to replace Adler32 hash for fast scanning overlapping words of similar chunks, (2) adopting a quick array-based indexing for word-matching, (3) applying a sampling indexing scheme to reduce the cost of traditional building full indexes for base chunks’ words, (4) skipping unmatched words to accelerate delta encoding through non-redundant areas, and (5) last but not least, after word-matching, further batch compressing the remainder to improve the compression ratio. Our evaluation results driven by seven real-world datasets suggest that Gdelta achieves encoding/decoding speedups of 3.5X ∼ 25X over the classic Xdelta and Zdelta approaches while increasing the compression ratio by about 10% ∼ 240%.</p>","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140928573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Memory-Disaggregated Radix Tree","authors":"Xuchuan Luo, Pengfei Zuo, Jiacheng Shen, Jiazhen Gu, Xin Wang, Michael Lyu, Yangfan Zhou","doi":"10.1145/3664289","DOIUrl":"https://doi.org/10.1145/3664289","url":null,"abstract":"<p>Disaggregated memory (DM) is an increasingly prevalent architecture with high resource utilization. It separates computing and memory resources into two pools and interconnects them with fast networks. Existing range indexes on DM are based on B+ trees, which suffer from large inherent read and write amplifications. The read and write amplifications rapidly saturate the network bandwidth, resulting in low request throughput and high access latency of B+ trees on DM. </p><p>In this paper, we propose that the radix tree is more suitable for DM than the B+ tree due to smaller read and write amplifications. However, constructing a radix tree on DM is challenging due to the costly lock-based concurrency control, the bounded memory-side IOPS, and the complicated computing-side cache validation. To address these challenges, we design <b>SMART</b>, the first radix tree for disaggregated memory with high performance. Specifically, we leverage 1) a <i>hybrid concurrency control</i> scheme including lock-free internal nodes and fine-grained lock-based leaf nodes to reduce lock overhead, 2) a computing-side <i>read-delegation and write-combining</i> technique to break through the IOPS upper bound by reducing redundant I/Os, and 3) a simple yet effective <i>reverse check</i> mechanism for computing-side cache validation. Experimental results show that SMART achieves 6.1 × higher throughput under typical write-intensive workloads and 2.8 × higher throughput under read-only workloads in YCSB benchmarks, compared with state-of-the-art B+ trees on DM.</p>","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140928577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fastmove: A Comprehensive Study of On-Chip DMA and its Demonstration for Accelerating Data Movement in NVM-based Storage Systems","authors":"Jiahao Li, Jingbo Su, Luofan Chen, Cheng Li, Kai Zhang, Liang Yang, Sam Noh, Yinlong Xu","doi":"10.1145/3656477","DOIUrl":"https://doi.org/10.1145/3656477","url":null,"abstract":"<p>Data-intensive applications executing on NVM-based storage systems experience serious bottlenecks when moving data between DRAM and NVM. We advocate for the use of the long-existing but recently neglected on-chip DMA to expedite data movement with three contributions. First, we explore new latency-oriented optimization directions, driven by a comprehensive DMA study, to design a high-performance DMA module, which significantly lowers the I/O size threshold to observe benefits. Second, we propose a new data movement engine, <monospace>Fastmove</monospace>, that coordinates the use of the DMA along with the CPU with DDIO-aware strategies, judicious scheduling and load splitting such that the DMA’s limitations are compensated, and the overall gains are maximized. Finally, with a general kernel-based design, simple APIs, and DAX file system integration, <monospace>Fastmove</monospace> allows applications to transparently exploit the DMA and its new features without code change. We run three data-intensive applications MySQL, GraphWalker, and Filebench atop <monospace>NOVA</monospace>, <monospace>ext4-DAX</monospace>, and <monospace>XFS-DAX</monospace>, with standard benchmarks like TPC-C, and popular graph algorithms like PageRank. Across single- and multi-socket settings, compared to the conventional CPU-only NVM accesses, <monospace>Fastmove</monospace> introduces to TPC-C with MySQL 1.13-2.16 × speedups of peak throughput, reduces the average latency by 17.7-60.8%, and saves 37.1-68.9% CPU usage spent in data movement. It also shortens the execution time of graph algorithms with GraphWalker by 39.7-53.4%, and introduces 1.01-1.48 × throughput speedups for Filebench.</p>","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140881717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FSDedup: Feature-Aware and Selective Deduplication for Improving Performance of Encrypted Non-Volatile Main Memory","authors":"Chunfeng Du, Zihang Lin, Suzhen Wu, Yifei Chen, Jiapeng Wu, Shengzhe Wang, Weichun Wang, Qingfeng Wu, Bo Mao","doi":"10.1145/3662736","DOIUrl":"https://doi.org/10.1145/3662736","url":null,"abstract":"<p>Enhancing the endurance, performance, and energy efficiency of encrypted Non-Volatile Main Memory (NVMM) can be achieved by minimizing written data through inline deduplication. However, existing approaches applying inline deduplication to encrypted NVMM suffer from substantial performance degradation due to high computing, memory footprint, and index-lookup overhead to generate, store, and query the cryptographic hash (fingerprint). In the preliminary ESD [14], we proposed the Error Correcting Code (ECC) assisted selective deduplication scheme, utilizing the ECC information as a fingerprint to identify similar data effectively and then leveraging the selective deduplication technique to eliminate a large amount of redundant data with high reference counts. In this paper, we proposed FSDedup. Compared with ESD, FSDedup could leverage the prefetch cache to reduce the read overhead during similarity comparison and utilize the cache refresh mechanism to identify further and eliminate more redundant data. Extensive experimental evaluations demonstrate that FSDedup can enhance the performance of the NVMM system further than the ESD. Experimental results show that FSDedup can improve both write and read speed by up to 1.8 ×, enhance Instructions Per Cycle (IPC) by up to 1.5 ×, and reduce energy consumption by up to 2.0 ×, compared to ESD.</p>","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Implementation of Deduplication on F2FS","authors":"Tiangmeng Zhang, Renhui Chen, Zijing Li, Congming Gao, Chengke Wang, Jiwu Shu","doi":"10.1145/3662735","DOIUrl":"https://doi.org/10.1145/3662735","url":null,"abstract":"<p>Data deduplication technology has gained popularity in modern file systems due to its ability to eliminate redundant writes and improve storage space efficiency. In recent years, the flash-friendly file system (F2FS) has been widely adopted in flash memory based storage devices, including smartphones, fast-speed servers and Internet of Things. In this paper, we propose F2DFS (deduplication-based F2FS), which introduces three main design contributions. First, F2DFS integrates inline and offline hybrid deduplication. Inline deduplication eliminates redundant writes and enhances flash device endurance, while offline deduplication mitigates the negative I/O performance impact and saves more storage space. Second, F2DFS follows the file system coupling design principle, effectively leveraging the potentials and benefits of both deduplication and native F2FS. Also, with the aid of this principle, F2DFS achieves high-performance and space-efficient incremental deduplication. Third, F2DFS adopts virtual indexing to mitigate deduplication-induced many-to-one mapping updates during the segment cleaning. We conducted comprehensive experimental comparisons between F2DFS, native F2FS, and other state-of-the-art deduplication schemes, using both synthetic and real-world workloads. For inline deduplication, F2DFS outperforms SmartDedup, Dmdedup, and ZFS, in terms of both I/O bandwidth performance and deduplication rates. And for offline deduplication, compared to SmartDedup, XFS and BtrFS, F2DFS shows higher execution efficiency, lower resource usage and greater storage space savings. Moreover, F2DFS demonstrates more efficient segment cleanings than native F2FS.</p>","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140812433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}