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    Adaptively detecting changes in Autonomic Grid Computing

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    Type
    Conference Paper
    Authors
    Zhang, Xiangliang cc
    Germain, Cécile
    Sebag, Michèle
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2010-10
    Permanent link to this record
    http://hdl.handle.net/10754/564313
    
    Metadata
    Show full item record
    Abstract
    Detecting the changes is the common issue in many application fields due to the non-stationary distribution of the applicative data, e.g., sensor network signals, web logs and gridrunning logs. Toward Autonomic Grid Computing, adaptively detecting the changes in a grid system can help to alarm the anomalies, clean the noises, and report the new patterns. In this paper, we proposed an approach of self-adaptive change detection based on the Page-Hinkley statistic test. It handles the non-stationary distribution without the assumption of data distribution and the empirical setting of parameters. We validate the approach on the EGEE streaming jobs, and report its better performance on achieving higher accuracy comparing to the other change detection methods. Meanwhile this change detection process could help to discover the device fault which was not claimed in the system logs. © 2010 IEEE.
    Citation
    Zhang, X., Germain, C., & Sebag, M. (2010). Adaptively detecting changes in Autonomic Grid Computing. 2010 11th IEEE/ACM International Conference on Grid Computing. doi:10.1109/grid.2010.5698017
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2010 11th IEEE/ACM International Conference on Grid Computing
    Conference/Event name
    2010 11th IEEE/ACM International Conference on Grid Computing, Grid 2010
    ISBN
    9781424493487
    DOI
    10.1109/GRID.2010.5698017
    ae974a485f413a2113503eed53cd6c53
    10.1109/GRID.2010.5698017
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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