Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Machine Intelligence & kNowledge Engineering Lab
Date
2010-10Permanent link to this record
http://hdl.handle.net/10754/564313
Metadata
Show full item recordAbstract
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.2010.5698017Conference/Event name
2010 11th IEEE/ACM International Conference on Grid Computing, Grid 2010ISBN
9781424493487ae974a485f413a2113503eed53cd6c53
10.1109/GRID.2010.5698017