Future Non-volatile Memory Storage Architecture and File System Interface

Octopus + : An RDMA-Enabled Distributed Persistent Memory File System

ACM Transactions on Storage ◽

10.1145/3448418 ◽

2021 ◽

Vol 17 (3) ◽

pp. 1-25

Author(s):

Bohong Zhu ◽

Youmin Chen ◽

Qing Wang ◽

Youyou Lu ◽

Jiwu Shu

Keyword(s):

High Speed ◽

High Performance ◽

File System ◽

Direct Memory Access ◽

File Systems ◽

Distributed File Systems ◽

Persistent Memory ◽

Memory Modules ◽

Non Volatile Memory ◽

Volatile Memory

Non-volatile memory and remote direct memory access (RDMA) provide extremely high performance in storage and network hardware. However, existing distributed file systems strictly isolate file system and network layers, and the heavy layered software designs leave high-speed hardware under-exploited. In this article, we propose an RDMA-enabled distributed persistent memory file system, Octopus + , to redesign file system internal mechanisms by closely coupling non-volatile memory and RDMA features. For data operations, Octopus + directly accesses a shared persistent memory pool to reduce memory copying overhead, and actively fetches and pushes data all in clients to rebalance the load between the server and network. For metadata operations, Octopus + introduces self-identified remote procedure calls for immediate notification between file systems and networking, and an efficient distributed transaction mechanism for consistency. Octopus + is enabled with replication feature to provide better availability. Evaluations on Intel Optane DC Persistent Memory Modules show that Octopus + achieves nearly the raw bandwidth for large I/Os and orders of magnitude better performance than existing distributed file systems.

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NVMTFS: A Non-Volatile Memory Adaptive File System for Tiered Storage System

2018 4th International Conference on Big Data Computing and Communications (BIGCOM) ◽

10.1109/bigcom.2018.00039 ◽

2018 ◽

Author(s):

Shiyong Liu ◽

Zhichao Cao ◽

Zhongwen Guo ◽

Guohua Wang ◽

Xupeng Wang ◽

...

Keyword(s):

File System ◽

Storage System ◽

Non Volatile Memory ◽

Volatile Memory ◽

Adaptive File System

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UMFS: An efficient user-space file system for non-volatile memory

Journal of Systems Architecture ◽

10.1016/j.sysarc.2018.04.004 ◽

2018 ◽

Vol 89 ◽

pp. 18-29 ◽

Cited By ~ 2

Author(s):

Xianzhang Chen ◽

Edwin H.-M. Sha ◽

Qingfeng Zhuge ◽

Ting Wu ◽

Weiwen Jiang ◽

...

Keyword(s):

File System ◽

Non Volatile Memory ◽

User Space ◽

Volatile Memory

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

ACM Transactions on Storage ◽

10.1145/3453300 ◽

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.

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