KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Machine Intelligence & kNowledge Engineering Lab
Permanent link to this recordhttp://hdl.handle.net/10754/564313
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AbstractDetecting 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.
CitationZhang, 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
Conference/Event name2010 11th IEEE/ACM International Conference on Grid Computing, Grid 2010