• 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
Wu Yihan, Huang Gang, Zhang Ying, Xiong Yingfei. A Model-Based Fault Tolerance Mechanism Development Approach for Cloud Computing[J]. Journal of Computer Research and Development, 2016, 53(1): 138-154. DOI: 10.7544/issn1000-1239.2016.20150608
Citation: Wu Yihan, Huang Gang, Zhang Ying, Xiong Yingfei. A Model-Based Fault Tolerance Mechanism Development Approach for Cloud Computing[J]. Journal of Computer Research and Development, 2016, 53(1): 138-154. DOI: 10.7544/issn1000-1239.2016.20150608

A Model-Based Fault Tolerance Mechanism Development Approach for Cloud Computing

More Information
  • Published Date: December 31, 2015
  • There are many different kinds of cloud computing platforms, such as CloudStack, OpenStack, Eucalyptus, and so on, which differentiate from each other in management abilities and management styles. Even in a particular cloud platform, there are also different kinds of virtualization technologies, such as Xen, KVM, VMware, etc. Recent years, with the rapid development of private cloud and hybrid cloud, the heterogeneity degree of infrastructure is aggravated. Fault tolerance (FT) mechanisms are usually supported by the management ability and management style of the infrastructure. As a result, a fault-tolerant mechanism needs to be repeatedly implemented on different platforms. Meanwhile, this directly causes the obvious growing difficulty and increasing amount of time consumption in FT mechanism. In order to reach the goal of achieving FT mechanism among different platforms, we propose a model-based, cross-platform FT mechanism development approach in this paper. To validate the effectiveness and practicability of model-based development approach, we implemente seven fault tolerance mechanisms in CloudStack and OpenStack. A series of experiments show that failover is implemented effectively by these FT mechanisms, and the reliability and availability of the FT target are improved. With high reusability (over 90%) of the code, the FT mechanisms in this thesis can function cross different platforms. Analysis of the questionnaire survey conducted among developers show that our approach can improve the development experience and development efficiency.
  • Related Articles

    [1]Zeng Weixin, Zhao Xiang, Tang Jiuyang, Tan Zhen, Wang Wei. Iterative Entity Alignment via Re-Ranking[J]. Journal of Computer Research and Development, 2020, 57(7): 1460-1471. DOI: 10.7544/issn1000-1239.2020.20190643
    [2]Dai Chenchao, Wang Hongyuan, Ni Tongguang, Chen Shoubing. Person Re-Identification Based on Deep Convolutional Generative Adversarial Network and Expanded Neighbor Reranking[J]. Journal of Computer Research and Development, 2019, 56(8): 1632-1641. DOI: 10.7544/issn1000-1239.2019.20190195
    [3]Du Ruizhong, Li Mingyue, Tian Junfeng. Multi-keyword Ranked Ciphertext Retrieval Scheme Based on Clustering Index[J]. Journal of Computer Research and Development, 2019, 56(3): 555-565. DOI: 10.7544/issn1000-1239.2019.20170830
    [4]Guo Jiafeng, Fan Yixing. Exploration on Neural Information Retrieval Framework[J]. Journal of Computer Research and Development, 2018, 55(9): 1987-1999. DOI: 10.7544/issn1000-1239.2018.20180133
    [5]Zhong Qi, Wang Jing, Guan Xuetao, Huang Tao, Wang Keyi. Data Object Scale Aware Rank-Level Memory Allocation[J]. Journal of Computer Research and Development, 2014, 51(3): 672-680.
    [6]Liu Xiping, Wan Changxuan, and Liu Dexi. Effective XML Vague Content and Structure Retrieval and Scoring[J]. Journal of Computer Research and Development, 2010, 47(6): 1070-1078.
    [7]Xu Cunlu, Chen Yanqiu, Lu Hanqing. Statistical Landscape Features for Texture Retrieval[J]. Journal of Computer Research and Development, 2006, 43(4): 702-707.
    [8]Xing Qiang, Yuan Baozong, and Tang Xiaofang. A Fast Image Retrieval Method Based on Weighted Chromaticity Histogram[J]. Journal of Computer Research and Development, 2005, 42(11): 1903-1910.
    [9]Ru Liyun, Ma Shaoping, and Lu Jing. Feature Fusion Based on the Average Precision in Image Retrieval[J]. Journal of Computer Research and Development, 2005, 42(9): 1640-1646.
    [10]Zhang Min, Lin Chuan, and Ma Shaoping. Dynamic Parameter Learning Approach for Information Retrieval with Genetic Algorithm[J]. Journal of Computer Research and Development, 2005, 42(3).

Catalog

    Article views (1823) PDF downloads (1153) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return