• 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
Liu Pei, Jiang Ziyi, Cao Xiu. Node Selection Algorithm During Multi-Nodes Repair Progress in Distributed Storage System[J]. Journal of Computer Research and Development, 2018, 55(7): 1557-1568. DOI: 10.7544/issn1000-1239.2018.20160915
Citation: Liu Pei, Jiang Ziyi, Cao Xiu. Node Selection Algorithm During Multi-Nodes Repair Progress in Distributed Storage System[J]. Journal of Computer Research and Development, 2018, 55(7): 1557-1568. DOI: 10.7544/issn1000-1239.2018.20160915

Node Selection Algorithm During Multi-Nodes Repair Progress in Distributed Storage System

More Information
  • Published Date: June 30, 2018
  • In distributed storage systems, how to optimize the regeneration time of lost data so as to keep high reliability has attracted attention increasingly. Recent researches reveal that node selection mechanism during repair progress has great impact on regeneration time. SPSN (select provider select newcomer) algorithm has put forward by some studies, which suits the scenario of single node failure well. However, it is very common to repair many modes at the same time in actual system. In this scenario, SPSN algorithm will no longer be optimal taking large time and space consumption into consideration. By analyzing the data failure trace of real distributed file system, we propose a node selection algorithm B-WSJ (bandwidth based weak and strong judgement) based on the existing algorithms and repairing model with the characteristic of parallelism which is suitable for multi-failure scenario. In order to describe the algorithm better, we firstly define several concepts of node-relationship on a link. Secondly we use these concepts to realize the weak and strong judgment of target node with pre-process and pruning strategy added. Finally, the nodes with better bandwidth will be chosen. To evaluate the performance of NS algorithm, we use Waxman algorithm to generate network topology and do many experiments with node failure models in real system provided by FTA (failure trace archive). The experimental results show the performance of B-WSJ algorithm can be improved greatly compared with the existing algorithms.
  • Related Articles

    [1]Li Ping, Song Shuhan, Zhang Yuan, Cao Huawei, Ye Xiaochun, Tang Zhimin. HSEGRL: A Hierarchical Self-Explainable Graph Representation Learning Model[J]. Journal of Computer Research and Development, 2024, 61(8): 1993-2007. DOI: 10.7544/issn1000-1239.202440142
    [2]Shang Jing, Wu Zhihui, Xiao Zhiwen, Zhang Yifei. Graph4Cache: A Graph Neural Network Model for Cache Prefetching[J]. Journal of Computer Research and Development, 2024, 61(8): 1945-1956. DOI: 10.7544/issn1000-1239.202440190
    [3]Zeng Biqing, Zeng Feng, Han Xuli, Shang Qi. Aspect Extraction Model Based on Interactive Feature Representation[J]. Journal of Computer Research and Development, 2021, 58(1): 224-232. DOI: 10.7544/issn1000-1239.2021.20190305
    [4]Zeng Yifu, Mu Qilin, Zhou Le, Lan Tian, Liu Qiao. Graph Embedding Based Session Perception Model for Next-Click Recommendation[J]. Journal of Computer Research and Development, 2020, 57(3): 590-603. DOI: 10.7544/issn1000-1239.2020.20190188
    [5]Meng Huanyu, Liu Zhen, Wang Fang, Xu Jiadong, Zhang Guoqiang. An Efficient Collaborative Filtering Algorithm Based on Graph Model and Improved KNN[J]. Journal of Computer Research and Development, 2017, 54(7): 1426-1438. DOI: 10.7544/issn1000-1239.2017.20160302
    [6]Peng Zhenlian, Wang Jian, He Keqing, Tang Mingdong. A Requirements Elicitation Approach Based on Feature Model and Collaborative Filtering[J]. Journal of Computer Research and Development, 2016, 53(9): 2055-2066. DOI: 10.7544/issn1000-1239.2016.20150426
    [7]Jia Dongyan and Zhang Fuzhi. A Collaborative Filtering Recommendation Algorithm Based on Double Neighbor Choosing Strategy[J]. Journal of Computer Research and Development, 2013, 50(5): 1076-1084.
    [8]Ou Xiaoping, Wang Chaokun, Peng Zhuo, Qiu Ping, and Bai Yiyuan. A Graph-Based Music Data Model and Query Language[J]. Journal of Computer Research and Development, 2011, 48(10): 1879-1889.
    [9]Li Xiaoguang and Song Baoyan. GPE: A Graph-Based Determination Model for Meaningful NFS Query Result[J]. Journal of Computer Research and Development, 2010, 47(1): 174-181.
    [10]Gao Ying, Qi Hong, Liu Jie, and Liu Dayou. A Collaborative Filtering Recommendation Algorithm Combining Probabilistic Relational Models and User Grade[J]. Journal of Computer Research and Development, 2008, 45(9): 1463-1469.
  • Cited by

    Periodical cited type(7)

    1. 李学龄,柴雁欣,萧展辉,包新晔. 面向项目全生命周期的语义融合模型的构建. 自动化技术与应用. 2025(01): 173-176+184 .
    2. 王法胜,贺冰,孙福明,周慧. 自适应内容感知空间正则化相关滤波跟踪算法. 吉林大学学报(工学版). 2024(10): 3037-3049 .
    3. 吴捷,马小虎. 基于稀疏约束与双线索选择的目标跟踪算法. 火力与指挥控制. 2023(02): 19-25 .
    4. 姜文涛,张博强. 通道和异常适应性的目标跟踪算法. 计算机科学与探索. 2023(07): 1644-1657 .
    5. 张博. 基于残差神经网络的目标运动边界视觉快速跟踪算法. 探测与控制学报. 2023(03): 37-42+50 .
    6. 全震,吕静. 城市道路绿化结构信息高精度提取仿真. 计算机仿真. 2023(06): 216-219+337 .
    7. 姜文涛,崔江磊. 旋转区域提议网络的孪生神经网络跟踪算法. 计算机工程与应用. 2022(24): 247-255 .

    Other cited types(12)

Catalog

    Article views (1000) PDF downloads (503) Cited by(19)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return