NV-Journaling: Locality-Aware Journaling Using Byte-Addressable Non-Volatile Memory

Chen, Cheng; Wei, Qingsong; Wong, Wan Hui; Wang, Chundong

doi:10.1109/tc.2019.2948004

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Modern journaling file systems are designed to use whole data block as the basic unit of the journal what may cause significant overhead. As the actual price of the next genaration NVM prohibits it to be used as standalone large-capacity memory device it may be beneficial to use it in hybrid DRAM/NVMM (non-volatile main memory) or NVDIMM solutions where NVMM may act as the external journal device for journaling files systems [37,39,40] or the journaling strategies may be altered to be more suitable for NVM dvices [41]. On the other hand maybe the new principles of designing file systems are required as in [42,43] to fully utilize the cutting-edge NVM media.…”

Section: Nvm-adapted File Systemsmentioning

confidence: 99%

Evaluating Methods of Transferring Large Datasets

Kopeć

2022

Supercomputing Frontiers

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Our society critically depends on data, Big Data. The humanity generates and moves data volumes larger than ever before and their increase is continuously accelerating. The goal of this research is to evaluate tools used for the transfer of large volumes of data. Bulk data transfer is a complex endeavour that requires not only sufficient network infrastructure, but also appropriate software, computing power and storage resources. We report on the series of storage benchmarks conducted using recently developed elbencho tool. The tests were conducted with an objective to understand and avoid I/O bottlenecks during data transfer operation. Subsequently Ethernet and InfiniBand networks performance was compared using Ohio State University bandwidth benchmark (OSU BW) and iperf3 tool. For comparison we also tested traditional (very inefficient) Linux scp and rsync commands as well as tools designed specifically to transfer large datasets more efficiently: bbcp and MDTMFTP. Additionally the impact of using simultaneous multi-threading and Ethernet jumbo frames on transfer rate was evaluated.

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Section: Nvm-adapted File Systemsmentioning

confidence: 99%

Evaluating Methods of Transferring Large Datasets

Kopeć

2022

Supercomputing Frontiers

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Asynchronous and Adaptive Checkpoint for WAL-based Data Storage Systems

Zhu,

Hu,

Wang

2023

2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)

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Crab-tree

Wang

Chattopadhyay

Brihadiswarn

2020

ACM Trans. Embed. Comput. Syst.

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In recent years, the next-generation non-volatile memory (NVM) technologies have emerged with DRAM-like byte addressability and disk-like durability. Computer architects have proposed to use them to build persistent memory that blurs the conventional boundary between volatile memory and non-volatile storage. However, ARM processors, ones that are widely used in embedded computing systems, start providing architectural supports to utilize NVM since ARMv8. In this article, we consider tailoring B+-tree for NVM operated by a 64-bit ARMv8 processor. We first conduct an empirical study of performance overhead in writing and reading data for a B+-tree with an ARMv8 processor, including the time cost of cache line flushes and memory fences for crash consistency as well as the execution time of binary search compared to that of linear search. We hence identify the key weaknesses in the design of B+-tree with ARMv8 architecture. Accordingly, we develop a new B+-tree variant, namely, <underline>c</underline> rash <underline>r</underline> ecoverable <underline>A</underline> RMv8-oriented <underline>B </underline>+-tree (Crab-tree). To insert and delete data at runtime, Crab-tree selectively chooses one of two strategies, i.e., copy on write and shifting in place, depending on which one causes less consistency cost. Crab-tree regulates a strict execution order in both strategies and recovers the tree structure in case of crashes. To further improve the performance of Crab-tree, we employ three methods to reduce software overhead, cache misses, and consistency cost, respectively. We have implemented and evaluated Crab-tree in Raspberry Pi 3 Model B+ with emulated NVM. Experiments show that Crab-tree significantly outperforms state-of-the-art B+-trees designed for persistent memory by up to 2.2× and 3.7× in write and read performances, respectively, with both consistency and scalability achieved.

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NV-Journaling: Locality-Aware Journaling Using Byte-Addressable Non-Volatile Memory

Cited by 4 publications

References 16 publications

Evaluating Methods of Transferring Large Datasets

Evaluating Methods of Transferring Large Datasets

Asynchronous and Adaptive Checkpoint for WAL-based Data Storage Systems

Crab-tree

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