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Processing in storage class memory 20

Processing in storage class memory

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proceedings of the 12th usenix conference on Hot Topics in storage and file Systems

作者： Joel Nider Craig Mustard Andrada Zoltan Alexandra Fedorova University of British Columbia

storage and memory technologies are experiencing unprecedented transformation. storage-class memory (SCM) delivers near-DRAM performance in non-volatile storage media and became commercially available in 2019. Unfortunately, software is not yet able to fully benefit from such high-performance storage. Processing-in-memory (PIM) aims to overcome the notorious memory wall; at the time of writing, hardware is close to being commercially available. this paper takes a position that PIM will become an integral part of future storage-class memories, so data processing can be performed in-storage, saving memory bandwidth and CPU cycles. Under that assumption, we identify typical data-processing tasks poised for in-storage processing, such as compression, encryption and format conversion. We present evidence supporting our assumption and present some feasibility experiments on new PIM hardware to show the potential.

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Lock-free collaboration support for cloud storage services with operation inference and transformation 20

Lock-free collaboration support for cloud storage services w...

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proceedings of the 18th usenix conference on file and storage technologies

作者： Jian Chen Minghao Zhao Zhenhua Li Ennan Zhai Feng Qian Hongyi Chen Yunhao Liu Tianyin Xu Tsinghua University Alibaba Group University of Minnesota Tsinghua University and Michigan State University UIUC

ISBN: (纸本)9781939133120

this paper studies how today's cloud storage services support collaborative file editing. As a tradeoff for transparency/user-friendliness, they do not ask collaborators to use version control systems but instead implement their own heuristics for handling conflicts, which however often lead to unexpected and undesired experiences. With measurements and reverse engineering, we unravel a number of their design and implementation issues as the root causes of poor experiences. Driven by the findings, we propose to reconsider the collaboration support of cloud storage services from a novel perspective of operations without using any locks. To enable this idea, we design intelligent approaches to the inference and transformation of users' editing operations, as well as optimizations to the maintenance of files' historic versions. We build an open-source system UFC2 (User-Friendly Collaborative Cloud) to embody our design, which can avoid most (98%) conflicts with little (2%) overhead.

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Quiver: an informed storage cache for deep learning 20

Quiver: an informed storage cache for deep learning

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proceedings of the 18th usenix conference on file and storage technologies

作者： Abhishek Vijaya Kumar Muthian Sivathanu Microsoft Research India

ISBN: (纸本)9781939133120

We introduce Quiver, an informed storage cache for deep learning training (DLT) jobs in a cluster of GPUs. Quiver employs domain-specific intelligence within the caching layer, to achieve much higher efficiency compared to a generic storage cache. First, Quiver uses a secure hash-based addressing to transparently reuse cached data across multiple jobs and even multiple users operating on the same dataset. Second, by co-designing with the deep learning framework (e.g., PyTorch), Quiver employs a technique of substitutable cache hits to get more value from the existing contents of the cache, thus avoiding cache thrashing when cache capacity is much smaller than the working set. third, Quiver dynamically prioritizes cache allocation to jobs that benefit the most from the caching. With a prototype implementation in PyTorch, we show that Quiver can significantly improve throughput of deep learning workloads.

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Can we store the whole world's data in DNA storage? 20

Can we store the whole world's data in DNA storage?

引用

proceedings of the 12th usenix conference on Hot Topics in storage and file Systems

作者： Bingzhe Li Nae Young Song Li Ou David H.C. Du Department of Computer Science and Engineering University of Minnesota Twin Cities Department of Pediatrics University of Minnesota Twin Cities

the total amount of data in the world has been increasing rapidly. However, the increase of data storage capacity is much slower than that of data generation. How to store and archive such a huge amount of data becomes critical and challenging. Synthetic Deoxyribonucleic Acid (DNA) storage is one of the promising candidates with high density and long-term preservation for archival storage systems. the existing works have focused on the achievable feasibility of a small amount of data when using DNA as storage. In this paper, we investigate the scalability and potentials of DNA storage when a huge amount of data, like all available data from the world, is to be stored. First, we investigate the feasible storage capability that can be achieved in a single DNA pool/tube based on current and future technologies. then, the indexing of DNA storage is explored. Finally, the metadata overhead based on future technology trends is also investigated.

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GoSeed: generating an optimal seeding plan for deduplicated storage 20

GoSeed: generating an optimal seeding plan for deduplicated ...

引用

proceedings of the 18th usenix conference on file and storage technologies

作者： Aviv Nachman Gala Yadgar Sarai Sheinvald Computer Science Department Technion Braude College of Engineering

ISBN: (纸本)9781939133120

Deduplication decreases the physical occupancy of files in a storage volume by removing duplicate copies of data chunks, but creates data-sharing dependencies that complicate standard storage management tasks. Specifically, data migration plans must consider the dependencies between files that are remapped to new volumes and files that are not. thus far, only greedy approaches have been suggested for constructing such plans, and it is unclear how they compare to one another and how much they can be *** set to bridge this gap for seeding--migration in which the target volume is initially empty. We present GoSeed, a formulation of seeding as an integer linear programming (ILP) problem, and three acceleration methods for applying it to real-sized storage volumes. Our experimental evaluation shows that, while the greedy approaches perform well on "easy" problem instances, the cost of their solution can be significantly higher than that of GoSeed's solution on "hard" instances, for which they are sometimes unable to find a solution at all.

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SS-CDC: A Two-stage Parallel Content-Defined Chunking for Deduplicating Backup storage 19

SS-CDC: A Two-stage Parallel Content-Defined Chunking for De...

引用

12th ACM International conference on Systems and storage (SYSTOR)

作者： Ni, Fan Lin, Xing Jiang, Song Univ Texas Arlington Arlington TX 76019 USA NetApp Sunnyvale CA 94089 USA

ISBN: (纸本)9781450367493

Data deduplication has been widely used in storage systems to improve storage efficiency and I/O performance. In particular, content-defined variable-size chunking ( CDC) is often used in data deduplication systems for its capability to detect and remove duplicate data in modified files. However, the CDC algorithm is very compute-intensive and inherently sequential. Efforts on accelerating it by segmenting a file and running the algorithm independently on each segment in parallel come at a cost of substantial degradation of deduplication ratio. In this paper, we propose SS-CDC, a two-stage parallel CDC, that enables ( almost) full parallelism on chunking of a file without compromising deduplication ratio. Further, SS-CDC exploits instruction-level SIMD parallelism available in today's processors. As a case study, by using Intel AVX-512 instructions, SS-CDC consistently obtains superlinear speedups on a multi-core server. Our experiments using real-world datasets show that, compared to existing parallel CDC methods which only achieve up to a 7.7x speedup on an 8-core processor with the deduplication ratio degraded by up to 40%, SS-CDC can achieve up to a 25.6x speedup with no loss of deduplication ratio.

关键词： storage Deduplication Content-defined-Chunking (CDC)

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Write Optimization of Log-structured Flash file System for Parallel I/O on Manycore Servers 19

Write Optimization of Log-structured Flash File System for P...

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12th ACM International conference on Systems and storage (SYSTOR)

作者： Lee, Chang-Gyu Byun, Hyunki Noh, Sunghyun Kang, Hyeongu Kim, Youngjae Sogang Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9781450367493

In Manycore server environment, we observe the performance degradation in parallel writes and identify the causes as follows - (i) When multiple threads write to a single file simultaneously, the current POSIX-based F2FS file system does not allow this parallel write even though ranges are distinct where threads are writing. ( ii) the high processing time of FSYNC at file system layer degrades the I/O throughput as multiple threads call FSYNC simultaneously. ( iii) the file system periodically checkpoints to recover from system crashes. All incoming I/O requests are blocked while the checkpoint is running, which significantly degrades overall file system performance. To solve these problems, first, we propose file systems to employ a fine-grained file-level Range Lock that allows multiple threads to write on mutually exclusive ranges of files rather than the course-grained inode mutex lock. Second, we propose NVM Node Logging that uses NVM as an extended storage space to store file metadata and file system metadata at high speed during FSYNC and checkpoint operations. In particular, the NVM Node Logging consists of ( i) a fine-grained inode structure to solve the write amplification problem caused by flushing the file metadata in block units and ( ii) a Pin Point NAT ( Node Address Table) Update, which can allow flushing only modified NAT entries. We implemented Range Lock and NVM Node Logging for F2FS in Linux kernel 4.14.11. Our extensive evaluation at two different types of servers ( single socket 10 cores CPU server, multi-socket 120 cores NUMA CPU server) shows significant write throughput improvements in both real and synthetic workloads.

关键词： Manycore OS file System Non-volatile Memory

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Carver: finding important parameters for storage system tuning 20

Carver: finding important parameters for storage system tuni...

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proceedings of the 18th usenix conference on file and storage technologies

作者： Zhen Cao Geoff Kuenning Erez Zadok Stony Brook University Harvey Mudd College

ISBN: (纸本)9781939133120

storage systems usually have many parameters that affect their behavior. Tuning those parameters can provide significant gains in performance. Alas, both manual and automatic tuning methods struggle due to the large number of parameters and exponential number of possible configurations. Since previous research has shown that some parameters have greater performance impact than others, focusing on a smaller number of more important parameters can speed up auto-tuning systems because they would have a smaller state space to explore. In this paper, we propose Carver, which uses (1) a variance-based metric to quantify storage parameters' importance, (2) Latin Hypercube Sampling to sample huge parameter spaces; and (3) a greedy but efficient parameter-selection algorithm that can identify important parameters. We evaluated Carver on datasets consisting of more than 500,000 experiments on 7 file systems, under 4 representative workloads. Carver successfully identified important parameters for all file systems and showed that importance varies with different workloads. We demonstrated that Carver was able to identify a near-optimal set of important parameters in our datasets. We showed Carver's efficiency by testing it with a small fraction of our dataset; it was able to identify the same set of important parameters with as little as 0.4% of the whole dataset.

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Towards Building a High-Performance, Scale-In Key-Value storage System 19

Towards Building a High-Performance, Scale-In Key-Value Stor...

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12th ACM International conference on Systems and storage (SYSTOR)

作者： Kang, Yangwook Pitchumani, Rekha Mishra, Pratik Kee, Yang-suk Londono, Francisco Oh, Sangyoon Lee, Jongyeol Lee, Daniel D. G. Samsung Semicond Inc San Jose CA USA

ISBN: (纸本)9781450367493

Key-value stores are widely used as storage backends, due to their simple, yet flexible interface for cache, storage, file system, and database systems. However, when used with high performance NVMe devices, their high compute requirements for data management often leave the device bandwidth under-utilized. this leads to a performance mismatch of what the device is capable of delivering and what it actually delivers, and the gains derived from high speed NVMe devices is nullified. In this paper, we introduce KV-SSD ( Key-Value SSD) as a key technology in a holistic approach to overcome such performance imbalance. KV-SSD provides better scalability and performance by simplifying the software storage stack and consolidating redundancy, thereby lowering the overall CPU usage and releasing the memory to user applications. We evaluate the performance and scalability of KV-SSDs over state-of-the-art software alternatives built for traditional block SSDs. Our results show that, unlike traditional key-value systems, the overall performance of KV-SSD scales linearly, and delivers 1.6 to 57x gains depending on the workload characteristics.

关键词： Key-value store Scalability Key-value SSD

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POLARDB meets computational storage: efficiently support analytical workloads in cloud-native relational database 20

POLARDB meets computational storage: efficiently support ana...

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proceedings of the 18th usenix conference on file and storage technologies

作者： Wei Cao Yang Liu Zhushi Cheng Ning Zheng Wei Li Wenjie Wu Linqiang Ouyang Peng Wang Yijing Wang Ray Kuan Zhenjun Liu Feng Zhu Tong Zhang Alibaba Group Hang Zhou Zhejiang China ScaleFlux Inc. San Jose CA

ISBN: (纸本)9781939133120

this paper reports the deployment of computational storage drives in Alibaba Cloud to enable cloud-native relational database cost-effectively support analytical workloads. With its compute-storage decoupled architecture, cloud-native relational database should pushdown data-intensive tasks (e.g., table scan) from front-end database nodes to back-end storage nodes in order to adequately support analytical workloads. this however makes it a challenge to maintain the cost effectiveness of storage nodes. the emerging computational storage opens a new opportunity to address this challenge: By replacing commodity SSDs with computational storage drives, storage nodes can leverage the in-storage computing power to much more efficiently perform table scans. Practical implementation of this simple idea is non-trivial and demands cohesive innovations across the software (i.e., database, filesystem and I/O) and hardware (i.e., computational storage drive) layers. this paper presents such a holistic implementation for Alibaba cloud-native relational database POLARDB. To the best of our knowledge, this is the first real-world deployment of cloud-native databases with computational storage drives ever reported in the open literature.

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