• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Chen Youmin, Lu Youyou, Luo Shengmei, Shu Jiwu. Survey on RDMA-Based Distributed Storage Systems[J]. Journal of Computer Research and Development, 2019, 56(2): 227-239. DOI: 10.7544/issn1000-1239.2019.20170849
Citation: Chen Youmin, Lu Youyou, Luo Shengmei, Shu Jiwu. Survey on RDMA-Based Distributed Storage Systems[J]. Journal of Computer Research and Development, 2019, 56(2): 227-239. DOI: 10.7544/issn1000-1239.2019.20170849

Survey on RDMA-Based Distributed Storage Systems

More Information
  • Published Date: January 31, 2019
  • RDMA (remote direct memory access) is being widely used in big data area, which allows local host to access the remote memory without the involvements of remote CPUs, and provides extremely high bandwidth, high throughput and low latency, thus helping to boost the performance of distributed storage systems dramatically. As a whole, the RDMA-enabled distributed storage systems bring new opportunity to the big data processing. In this paper, we firstly point out that simply replacing the network module in distributed systems cannot fully exploit the advantages of RDMA in both semantics and efficiency, and revolutions of storage system design are urgently needed. Then, two key aspects of efficiently using RDMA are illustrated: One is the efficient management of hardware resources, including the careful utilization of NIC an CPU cache, parallel acceleration of multicore CPUs and memory management, and the other is the reformation of the software by closely coupling the software design and RDMA semantics, which uses the new features of RDMA to redesign the data placement schemes, data indexing and distributed protocols. Relative research works of distributed file systems, distributed key-value stores, and distributed transactional systems are introduced to illustrate the above two aspects. Summarizes of the paper, and suggestions for future research are also given at the end of this paper.
  • Related Articles

    [1]Chen Yuming, Li Wei. Granular Vectors and K Nearest Neighbor Granular Classifiers[J]. Journal of Computer Research and Development, 2019, 56(12): 2600-2611. DOI: 10.7544/issn1000-1239.2019.20180572
    [2]Wang Nian, Peng Zhenghong, Cui Li. EasiFFRA: A Fast Feature Reduction Algorithm Based on Neighborhood Rough Set[J]. Journal of Computer Research and Development, 2019, 56(12): 2578-2588. DOI: 10.7544/issn1000-1239.2019.20180541
    [3]Luo Sheng, Miao Duoqian, Zhang Zhifei, Zhang Yuanjian, Hu Shengdan. A Link Prediction Model Based on Hierarchical Information Granular Representation for Attributed Graphs[J]. Journal of Computer Research and Development, 2019, 56(3): 623-634. DOI: 10.7544/issn1000-1239.2019.20170961
    [4]Deng Dayong, Miao Duoqian, Huang Houkuan. Analysis of Concept Drifting and Uncertainty in an Information Table[J]. Journal of Computer Research and Development, 2016, 53(11): 2607-2612. DOI: 10.7544/issn1000-1239.2016.20150803
    [5]Fu Zhiyao, Gao Ling, Sun Qian, Li Yang, Gao Ni. Evaluation of Vulnerability Severity Based on Rough Sets and Attributes Reduction[J]. Journal of Computer Research and Development, 2016, 53(5): 1009-1017. DOI: 10.7544/issn1000-1239.2016.20150065
    [6]Zhang Zhifei, Miao Duoqian, Nie Jianyun, Yue Xiaodong. Sentiment Uncertainty Measure and Classification of Negative Sentences[J]. Journal of Computer Research and Development, 2015, 52(8): 1806-1816. DOI: 10.7544/issn1000-1239.2015.20150253
    [7]Zhu Hong, Ding Shifei, Xu Xinzheng. An AP Clustering Algorithm of Fine-Grain Parallelism Based on Improved Attribute Reduction[J]. Journal of Computer Research and Development, 2012, 49(12): 2638-2644.
    [8]Wang Xizhao, Wang Tingting, and Zhai Junhai. An Attribute Reduction Algorithm Based on Instance Selection[J]. Journal of Computer Research and Development, 2012, 49(11): 2305-2310.
    [9]Yang Bin and Xu Baowen. Distributive Reduction of Attributes in Concept Lattice[J]. Journal of Computer Research and Development, 2008, 45(7).
    [10]Shang Lin, Wan Qiong, Yao Wangshu, Wang Jingen, Chen Shifu. An Approach for Reduction of Continuous-Valued Attributes[J]. Journal of Computer Research and Development, 2005, 42(7): 1217-1224.
  • Cited by

    Periodical cited type(10)

    1. 徐怡,陶强. 划分序乘积空间约简算法研究. 系统工程理论与实践. 2025(02): 554-570 .
    2. 刘长顺,刘炎,宋晶晶,徐泰华. 基于论域离散度的属性约简算法. 山东大学学报(理学版). 2023(05): 26-35+52 .
    3. 张清华,艾志华,张金镇. 融合密度与邻域覆盖约简的分类方法. 陕西师范大学学报(自然科学版). 2022(03): 33-42 .
    4. 张雨新,孙达明,李飞. 基于粒化单调的不完备混合型数据增量式属性约简算法. 计算机应用与软件. 2021(03): 279-286 .
    5. 邹丽,任思远,杨光,杨鑫华. 基于改进条件邻域熵的接头疲劳寿命影响因素分析. 焊接学报. 2021(11): 43-50+99-100 .
    6. 刘正,陈雪勤,张书锋. 基于最小化邻域互信息的邻域熵属性约简算法. 微电子学与计算机. 2020(03): 26-32 .
    7. 陈帅,张贤勇,唐玲玉,姚岳松. 邻域互补信息度量及其启发式属性约简. 数据采集与处理. 2020(04): 630-641 .
    8. 周艳红,张强. 基于三层粒结构的三支邻域熵. 数学的实践与认识. 2020(14): 83-93 .
    9. 亓慧,史颖. 不同度量下集成属性选择器的对比研究. 山西大学学报(自然科学版). 2019(04): 848-853 .
    10. 周艳红,张迪,张强. 基于单调信息度量的特定类属性约简. 内江师范学院学报. 2019(12): 35-39 .

    Other cited types(11)

Catalog

    Article views (3742) PDF downloads (1990) Cited by(21)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return