Adaptively detecting changes in Autonomic Grid Computing

Handle URI:
http://hdl.handle.net/10754/564313
Title:
Adaptively detecting changes in Autonomic Grid Computing
Authors:
Zhang, Xiangliang ( 0000-0002-3574-5665 ) ; Germain, Cécile; Sebag, Michèle
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Machine Intelligence & kNowledge Engineering Lab
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
Issue Date:
Oct-2010
DOI:
10.1109/GRID.2010.5698017
Type:
Conference Paper
ISSN:
15505510
ISBN:
9781424493487
Appears in Collections:
Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorZhang, Xiangliangen
dc.contributor.authorGermain, Cécileen
dc.contributor.authorSebag, Michèleen
dc.date.accessioned2015-08-04T06:23:14Zen
dc.date.available2015-08-04T06:23:14Zen
dc.date.issued2010-10en
dc.identifier.isbn9781424493487en
dc.identifier.issn15505510en
dc.identifier.doi10.1109/GRID.2010.5698017en
dc.identifier.urihttp://hdl.handle.net/10754/564313en
dc.description.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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleAdaptively detecting changes in Autonomic Grid Computingen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Laben
dc.identifier.journal2010 11th IEEE/ACM International Conference on Grid Computingen
dc.conference.date25 October 2010 through 29 October 2010en
dc.conference.name2010 11th IEEE/ACM International Conference on Grid Computing, Grid 2010en
dc.conference.locationBrusselsen
dc.contributor.institutionTAO - LRI, CNRS, Université Paris-Sud 11, Franceen
kaust.authorZhang, Xiangliangen
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