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» Issues in applying data mining to grid job failure detection...
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HPDC
2008
IEEE
13 years 11 months ago
Issues in applying data mining to grid job failure detection and diagnosis
As grid computation systems become larger and more complex, manually diagnosing failures in jobs becomes impractical. Recently, machine-learning techniques have been proposed to d...
Lakshmikant Shrinivas, Jeffrey F. Naughton
ICAC
2005
IEEE
13 years 10 months ago
Distributed Troubleshooting Agents
Key issues to address in autonomic job recovery for cluster computing are recognizing job failure; understanding the failure sufficiently to know if and how to restart the job; an...
Charles Earl, Emilio Remolina, Jim Ong, John Brown
GRID
2008
Springer
13 years 5 months ago
Troubleshooting thousands of jobs on production grids using data mining techniques
Large scale production computing grids introduce new challenges in debugging and troubleshooting. A user that submits a workload consisting of tens of thousands of jobs to a grid ...
David A. Cieslak, Nitesh V. Chawla, Douglas Thain
KDD
2005
ACM
193views Data Mining» more  KDD 2005»
14 years 5 months ago
An approach to spacecraft anomaly detection problem using kernel feature space
Development of advanced anomaly detection and failure diagnosis technologies for spacecraft is a quite significant issue in the space industry, because the space environment is ha...
Ryohei Fujimaki, Takehisa Yairi, Kazuo Machida
DMIN
2006
124views Data Mining» more  DMIN 2006»
13 years 6 months ago
Use of Multivariate Data Analysis for Lumber Drying Process Monitoring and Fault Detection
Process monitoring refers to the task of detecting abnormal process operations resulting from the shift in the mean and/or the variance of one or more process variables. To success...
Mouloud Amazouz, Radu Pantea