• 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
Fu Lingxiao, Peng Xin, and Zhao Wenyun. An Agent-Based Requirements Monitoring Framework for Internetware[J]. Journal of Computer Research and Development, 2013, 50(5): 1055-1065.
Citation: Fu Lingxiao, Peng Xin, and Zhao Wenyun. An Agent-Based Requirements Monitoring Framework for Internetware[J]. Journal of Computer Research and Development, 2013, 50(5): 1055-1065.

An Agent-Based Requirements Monitoring Framework for Internetware

More Information
  • Published Date: May 14, 2013
  • Running in a complicated, open and highly-dynamic environment, Internetware systems are likely to deviate from their requirements specification. In recent years, there have been a series of researches on runtime requirements monitoring and self-repairing based on goal-oriented requirements models and goal reasoning. However, a practical implementation framework for requirements monitoring and repairing, which supports typical Internetware characteristics like distribution and sociality, is still missing. In this paper, we propose an agent-based requirements monitoring framework for Internetware. The monitoring agents in the framework are able to monitor host systems on internal goal satisfaction and cross-agent goal delegation at runtime, and perform actuate repairing actions based on customized policies when requirements deviations are detected in a non-intrusive manner. The framework organizes monitoring agents in a decentralized way and supports cross-system goal delegation, requirements monitoring and self-repairing with inter-agent communication and interaction. To evaluate the effectiveness of our framework, we’ve conducted a case study with an online product booking system. The results show that the framework can effectively alleviate potential system failures in various self-reparing scenarios.
  • Related Articles

    [1]Xu Kai, Wu Xiaojun, Yin Hefeng. Distributed Low Rank Representation-Based Subspace Clustering Algorithm[J]. Journal of Computer Research and Development, 2016, 53(7): 1605-1611. DOI: 10.7544/issn1000-1239.2016.20148362
    [2]Tang Chenghua, Liu Pengcheng, Tang Shensheng, Xie Yi. Anomaly Intrusion Behavior Detection Based on Fuzzy Clustering and Features Selection[J]. Journal of Computer Research and Development, 2015, 52(3): 718-728. DOI: 10.7544/issn1000-1239.2015.20130601
    [3]Yang Xinxin, Huang Shaobin. A Hierarchical Co-Clustering Algorithm for High-Order Heterogeneous Data[J]. Journal of Computer Research and Development, 2015, 52(1): 200-210. DOI: 10.7544/issn1000-1239.2015.20130493
    [4]Li Suke and Jiang Yanbing. Semi-Supervised Sentiment Classification Based on Sentiment Feature Clustering[J]. Journal of Computer Research and Development, 2013, 50(12): 2570-2577.
    [5]Lu Weiming, Du Chenyang, Wei Baogang, Shen Chunhui, and Ye Zhenchao. Distributed Affinity Propagation Clustering Based on MapReduce[J]. Journal of Computer Research and Development, 2012, 49(8): 1762-1772.
    [6]Ling Ping, Wang Zhe, Zhou Chunguang, Huang Lan. Reduced Support Vector Clustering[J]. Journal of Computer Research and Development, 2010, 47(8): 1372-1381.
    [7]Zhang Gang, Liu Yue, Guo Jiafeng, and Cheng Xueqi. A Hierarchical Search Result Clustering Method[J]. Journal of Computer Research and Development, 2008, 45(3): 542-547.
    [8]Ding Shifei, Shi Zhongzhi, Jin Fengxiang, Xia Shixiong. A Direct Clustering Algorithm Based on Generalized Information Distance[J]. Journal of Computer Research and Development, 2007, 44(4): 674-679.
    [9]Ni Weiwei, Lu Jieping, and Sun Zhihui. An Effective Distributed k-Means Clustering Algorithm Based on the Pretreatment of Vectors' Inner-Product[J]. Journal of Computer Research and Development, 2005, 42(9): 1493-1497.
    [10]Liu Tao, Wu Gongyi, Chen Zheng. An Effective Unsupervised Feature Selection Method for Text Clustering[J]. Journal of Computer Research and Development, 2005, 42(3).

Catalog

    Article views (802) PDF downloads (516) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return