• 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
Zhang Qiang, Liang Jie, Xu Yinlong, Li Yongkun. Research of SSD Array Architecture Based on Workload Awareness[J]. Journal of Computer Research and Development, 2019, 56(4): 755-766. DOI: 10.7544/issn1000-1239.2019.20170832
Citation: Zhang Qiang, Liang Jie, Xu Yinlong, Li Yongkun. Research of SSD Array Architecture Based on Workload Awareness[J]. Journal of Computer Research and Development, 2019, 56(4): 755-766. DOI: 10.7544/issn1000-1239.2019.20170832

Research of SSD Array Architecture Based on Workload Awareness

More Information
  • Published Date: March 31, 2019
  • The fixed data layout of traditional array system and the locality of workloads cause the partial disks of the array system to become hot disks, which affects the reliability and the overall concurrency performance of the array system. This paper proposes a new RAID architecture for SSD array systems, HA-RAID, which leverages hot/cold data separation and sliding window techniques. The main idea is that HA-RAID divides the disk array into hot disks and ordinary disks, which stores hot data on hot disks and cold data on ordinary disks, and it changes the role of each disk dynamically by moving a fixed-length sliding window. So, each disk has the opportunity to become a hot disk and stores hot data which achieves the purpose of storing hot data evenly on each disk. Experiments under real-world workloads on a RAID-0 array system composed of eight commercial SSDs show that HA-RAID can achieve an even distribution of hot data across all disks and reduce the percentage of hot disks appearing in the array to almost zero. This implies that HA-RAID achieves load balance and wear balance at the device level. In terms of performance, HA-RAID reduces the average response time by 12.01%~41.06% which achieves the I/O performance enhancement, compared with traditional RAID-0 array.
  • Related Articles

    [1]Liang Bin, Li Guanghui, Dai Chenglong. G-mean Weighted Classification Method for Imbalanced Data Stream with Concept Drift[J]. Journal of Computer Research and Development, 2022, 59(12): 2844-2857. DOI: 10.7544/issn1000-1239.20210471
    [2]Liang Xinyan, Qian Yuhua, Guo Qian, Huang Qin. Multi-Granulation Fusion-Driven Method for Many-View Classification[J]. Journal of Computer Research and Development, 2022, 59(8): 1653-1667. DOI: 10.7544/issn1000-1239.20211112
    [3]Zhang Litian, Kong Jiayi, Fan Yihang, Fan Lingjun, Bao Ergude. Car Accident Prediction Based on Macro and Micro Factors in Probability Level[J]. Journal of Computer Research and Development, 2021, 58(9): 2052-2061. DOI: 10.7544/issn1000-1239.2021.20200345
    [4]Ju Zhuoya, Wang Zhihai. A Bayesian Classification Algorithm Based on Selective Patterns[J]. Journal of Computer Research and Development, 2020, 57(8): 1605-1616. DOI: 10.7544/issn1000-1239.2020.20200196
    [5]Wang Zhenwen, Xiao Weidong, and Tan Wentang. Classification in Networked Data Based on the Probability Generative Model[J]. Journal of Computer Research and Development, 2013, 50(12): 2642-2650.
    [6]Zhang Zhancheng, Wang Shitong, Fu-Lai Chung. Collaborative Classification Mechanism for Privacy-Preserving[J]. Journal of Computer Research and Development, 2011, 48(6): 1018-1028.
    [7]Huo Weigang, Shao Xiuli. A Fuzzy Associative Classification Method Based on Multi-Objective Evolutionary Algorithm[J]. Journal of Computer Research and Development, 2011, 48(4): 567-575.
    [8]Zou Quan, Guo Maozu, Liu Yang, and Wang Jun. A Classification Method for Class-Imbalanced Data and Its Application on Bioinformatics[J]. Journal of Computer Research and Development, 2010, 47(8): 1407-1414.
    [9]Ge Weiping, Wang Wei, Zhou Haofeng, and Shi Baile. Privacy Preserving Classification Mining[J]. Journal of Computer Research and Development, 2006, 43(1): 39-45.
    [10]Wu Gaowei, Tao Qing, Wang Jue. Support Vector Machines Based on Posteriori Probability[J]. Journal of Computer Research and Development, 2005, 42(2): 196-202.
  • Cited by

    Periodical cited type(9)

    1. 陈城,裴慧坤,刘丙财,林国安,魏恩伟,温启良. 基于公共边缘节点的输电物联网网关异构协议适配方法研究. 电测与仪表. 2024(11): 142-147 .
    2. 许明宇,王宜怀. 异构物联网中关联数据一致性规则挖掘模型. 计算机仿真. 2023(02): 425-428+442 .
    3. 常伟鹏,袁泉. 融合多模式匹配的网络信息实体关联研究仿真. 计算机仿真. 2021(01): 331-335 .
    4. 马早霞,李磊,刘心. 基于LoRaWAN协议的双向认证接入机制的研究. 河北工程大学学报(自然科学版). 2021(01): 92-98 .
    5. 汪滢,熊璐,刘晓. 基于大数据处理的模式匹配算法效率分析. 现代电子技术. 2021(09): 124-128 .
    6. 屈春一. 非均质性海量复杂异构数据的混合云存储技术. 单片机与嵌入式系统应用. 2021(08): 26-30 .
    7. 吴进伟,苏恺,董文斌. 基于混沌反馈控制的物联网配网物资数据选择算法研究. 电子设计工程. 2020(12): 105-108+113 .
    8. 韩高峰. 智能网络系统低匹配度数据深度挖掘算法研究. 宁夏师范学院学报. 2020(04): 82-88 .
    9. 张瑾. 电能计量在生活中的重要性研究. 电子元器件与信息技术. 2019(05): 99-102 .

    Other cited types(1)

Catalog

    Article views PDF downloads Cited by(10)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return