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47. WA-OPShare: Workload-Adaptive Over-Provisioning Space Allocation for Multi-Tenant SSDs.

Author

Wen, Yuhong, Zhou, You, Wu, Fei, Li, Shu, Wang, Zhenghong, and Xie, Changsheng

Subjects
REFUSE collection, SOLID state drives, PARALLEL processing
Abstract

Sharing a flash-based solid-state drive (SSD) among multiple tenants has become a common practice to improve storage utilization and cost efficiency. Meanwhile, how to allocate limited storage resources, especially the over-provisioning space (OPS) resources, among competitive tenants has emerged as a critical problem. The OPS refers to additional user-invisible storage space, whose size influences garbage collection (GC) efficiency. Due to unawareness of workload characteristics of different tenants, prior studies on multitenant OPS allocation lead to suboptimal SSD performance. In this article, we propose a novel workload-adaptive OPS allocation scheme for multitenant SSDs, called WA-OPShare. It targets an OPS sharing scheme that dynamically allocates the OPS among tenants to improve overall SSD performance. Two models are developed to identify underutilized storage space and predict the OPS-induced performance benefit of each tenant, respectively. Guided by the models, WA-OPShare regularly releases the underutilized storage space and then reallocates it to the tenant who can benefit the most. Experimental results show that compared to the traditional Partition and Sharing schemes, WA-OPShare improves the performance by up to 40.3% and 31.2%, and reduces the write amplification by up to 37.0% and 17.5%, respectively. [ABSTRACT FROM AUTHOR]

Published
2022
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48. Understanding and Exploiting the Full Potential of SSD Address Remapping.

Author

Wu, Qiulin, Zhou, You, Wu, Fei, Jiang, Hong, Zhou, Jian, and Xie, Changsheng

Subjects
SOLID state drives, FLASH memory, REFUSE collection, DATA corruption, RANDOM access memory
Abstract

Duplicate writes are prevalent in storage systems, originating from data duplication, journaling, and data relocations, etc. As flash-based solid state drives (SSDs) have been widely deployed, duplicate writes can significantly degrade their performance and lifetime. Prior studies have proposed innovative approaches that exploit the address remapping utility inside an SSD to eliminate duplicate writes. However, remap operations modify the logical-to-physical (L2P) address mapping table while the physical-to-logical (P2L) mappings persisted on flash memory remain unchanged. Such inconsistency between L2P and P2L mappings may cause data corruption and has long been a major obstacle to utilize SSD address remapping. In this article, we propose a novel SSD design, called Remap-SSD-LH, that realizes the full potential of SSD address remapping. It provides a remap primitive, which allows the host software and SSD firmware to perform logical writes of duplicate data at almost zero cost. To ensure mapping consistency as well as fast mapping lookups, Remap-SSD-LH employs a local log scheme based on hybrid storage. A local log is maintained for each flash garbage collection unit to record relevant P2L mapping changes induced by remap operations. The logs are stored in small nonvolatile RAM (NVRAM), e.g., capacitor-protected DRAM, and can be destaged to flash memory if NVRAM is full. We verify Remap-SSD-LH on a software SSD emulator with three case studies: 1) intra-SSD deduplication; 2) SQLite journaling; and 3) F2FS cleaning. The experimental results show that Remap-SSD-LH can maximally and efficiently exploit address remapping to improve SSD performance and lifetime. [ABSTRACT FROM AUTHOR]

Published
2022
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49. Optimal Program-Read Schemes Toward Highly Reliable Open Block Operations in 3-D Charge-Trap NAND Flash Memory.

Author

Jia, Menghua, Kong, Yachen, Zhan, Xuepeng, Zhang, Meng, Wu, Fei, and Chen, Jiezhi

Subjects
FLASH memory, BIT error rate, LITERARY criticism
Abstract

3-D NAND flash memory with vertically stacked layers has been widely applied benefiting from its large capacities and high performances. Recently, a novel open block operation scheme was proposed for further improvements of the utilization efficiency in large capacity blocks. In this article, reliability issues of the open block operation in 3-D charge-trap (CT) NAND flash memory are studied by focusing on the high raw bit error rates (RBERs) in the last programmed word-line (WL), which is named as the edge WL (EWL). By systematical characterizations, it is concluded that high RBER in the EWL originates from lateral charge migration (LCM) due to the special structure of 3-D CT NAND flash. To suppress the RBER in EWL, we propose the extra read (ER) and extra program (EP) schemes to compensate for the charge loss from LCM. The experimental results show that the RBER of EWL can be reduced by an average of 59.8% and 86.5% after adopting ER and EP schemes, respectively. Furthermore, for the highly reliable open block, we design a targeted low-density parity-check (LDPC) operation process to enhance the correction capability. By using these two methods, experimental results show that the error correction capabilities of the LDPC hard decoding are increased by 1.92 and 4.76 times, respectively. [ABSTRACT FROM AUTHOR]

Published
2022
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50. Local-Global Graph Pooling via Mutual Information Maximization for Video-Paragraph Retrieval.

Author

Zhang, Pengcheng, Zhao, Zhou, Wang, Nannan, Yu, Jun, and Wu, Fei

Subjects
VIDEOS, FEATURE extraction
Abstract

As a task of cross-modal retrieval between long videos and paragraphs, video-paragraph retrieval is a non-trivial task. Unlike traditional video-text retrieval, the video in video-paragraph retrieval usually contains multiple clips. Each clip corresponds to a descriptive sentence; all the sentences constitute the corresponding paragraph of the video. Previous methods for video-paragraph retrieval usually encode videos and para-graphs from segment-level (clips and sentences) and overall-level (videos and paragraphs). However, there are also contents about actions and objects that exist in the segment. Hence, we propose a Local-Global Graph Pooling Network (LGGP) via Mutual Information Maximization for video-paragraph retrieval. Our model disentangles videos and paragraphs into four levels: overall-level, segment-level, motion-level, and object-level. We construct the Hierarchical Local Graph (segment-level, motion-level, and object-level) and the Hierarchical Global Graph (overall-level, segment-level, motion-level, and object-level), respectively, for semantic interaction among different levels. Meanwhile, to obtain hierarchical pooling features with fine-grained semantic information, we design hierarchical graph pooling methods to maximize the mutual information between pooling features and corresponding graph nodes. We evaluate our model on two video-paragraph retrieval datasets with three different video features. The experimental results show that our model establishes state-of-the-art results for video-paragraph retrieval. Our code will be released at https://github.com/PengchengZhang1997/LGGP. [ABSTRACT FROM AUTHOR]

Published
2022
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