A novel non-volatile memory storage system for I/O-intensive applications

2018 ◽  
Vol 19 (10) ◽  
pp. 1291-1302 ◽  
Author(s):  
Wen-Bing Han ◽  
Xiao-Gang Chen ◽  
Shun-Fen Li ◽  
Ge-Zi Li ◽  
Zhi-Tang Song ◽  
...  
Keyword(s):  
Storage System ◽  
Memory Storage ◽  
Non Volatile Memory ◽  
Volatile Memory

NVLSM: A Persistent Memory Key-Value Store Using Log-Structured Merge Tree with Accumulative Compaction

2021 ◽  
Vol 17 (3) ◽  
pp. 1-26
Author(s):  
Baoquan Zhang ◽  
David H. C. Du
Keyword(s):  
Computer Systems ◽  
Memory Storage ◽  
Low Latency ◽  
Latency Data ◽  
Persistent Memory ◽  
Write Amplification ◽  
Non Volatile Memory ◽  
Data Persistence ◽  
Average Latency ◽  
Volatile Memory

Computer systems utilizing byte-addressable Non-Volatile Memory ( NVM ) as memory/storage can provide low-latency data persistence. The widely used key-value stores using Log-Structured Merge Tree ( LSM-Tree ) are still beneficial for NVM systems in aspects of the space and write efficiency. However, the significant write amplification introduced by the leveled compaction of LSM-Tree degrades the write performance of the key-value store and shortens the lifetime of the NVM devices. The existing studies propose new compaction methods to reduce write amplification. Unfortunately, they result in a relatively large read amplification. In this article, we propose NVLSM, a key-value store for NVM systems using LSM-Tree with new accumulative compaction. By fully utilizing the byte-addressability of NVM, accumulative compaction uses pointers to accumulate data into multiple floors in a logically sorted run to reduce the number of compactions required. We have also proposed a cascading searching scheme for reads among the multiple floors to reduce read amplification. Therefore, NVLSM reduces write amplification with small increases in read amplification. We compare NVLSM with key-value stores using LSM-Tree with two other compaction methods: leveled compaction and fragmented compaction. Our evaluations show that NVLSM reduces write amplification by up to 67% compared with LSM-Tree using leveled compaction without significantly increasing the read amplification. In write-intensive workloads, NVLSM reduces the average latency by 15.73%–41.2% compared to other key-value stores.

scholarly journals Microwave oven fabricated hybrid memristor devices for non-volatile memory storage

2014 ◽  
Vol 1 (4) ◽  
pp. 046305 ◽  
Author(s):  
E Verrelli ◽  
R J Gray ◽  
M O’Neill ◽  
S M Kelly ◽  
N T Kemp
Keyword(s):  
Microwave Oven ◽  
Memory Storage ◽  
Non Volatile Memory ◽  
Volatile Memory

scholarly journals Mapping Datasets to Object Storage System

2020 ◽  
Vol 245 ◽  
pp. 04037
Author(s):  
Xiaowei Aaron Chu ◽  
Jeff LeFevre ◽  
Aldrin Montana ◽  
Dana Robinson ◽  
Quincey Koziol ◽  
...  
Keyword(s):  
Storage Systems ◽  
Distributed Storage ◽  
Storage System ◽  
Random Access ◽  
Data Partitioning ◽  
Data Layout ◽  
Storage Devices ◽  
Storage Servers ◽  
Non Volatile Memory ◽  
Volatile Memory

Access libraries such as ROOT[1] and HDF5[2] allow users to interact with datasets using high level abstractions, like coordinate systems and associated slicing operations. Unfortunately, the implementations of access libraries are based on outdated assumptions about storage systems interfaces and are generally unable to fully benefit from modern fast storage devices. For example, access libraries often implement buffering and data layout that assume that large, single-threaded sequential access patterns are causing less overall latency than small parallel random access: while this is true for spinning media, it is not true for flash media. The situation is getting worse with rapidly evolving storage devices such as non-volatile memory and ever larger datasets. This project explores distributed dataset mapping infrastructures that can integrate and scale out existing access libraries using Ceph’s extensible object model, avoiding re-implementation or even modifications of these access libraries as much as possible. These programmable storage extensions coupled with our distributed dataset mapping techniques enable: 1) access library operations to be offloaded to storage system servers, 2) the independent evolution of access libraries and storage systems and 3) fully leveraging of the existing load balancing, elasticity, and failure management of distributed storage systems like Ceph. They also create more opportunities to conduct storage server-local optimizations specific to storage servers. For example, storage servers might include local key/value stores combined with chunk stores that require different optimizations than a local file system. As storage servers evolve to support new storage devices like non-volatile memory, these server-local optimizations can be implemented while minimizing disruptions to applications. We will report progress on the means by which distributed dataset mapping can be abstracted over particular access libraries, including access libraries for ROOT data, and how we address some of the challenges revolving around data partitioning and composability of access operations.

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