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Cluster Computing and the Grid, IEEE International Symposium on (2009)
Shanghai, China
May 18, 2009 to May 21, 2009
ISBN: 978-0-7695-3622-4
pp: 420-427
ABSTRACT
The ever increasing scale and complexity of large computational systems ask for sophisticated management tools, paving the way toward Autonomic Computing. A first step toward Autonomic Grids is presented in this paper; the interactions between the grid middleware and the stream of computational queries are modeled using statistical learning. The approach is implemented and validated in the context of the EGEE grid. The GStrAP system, embedding the StrAP Data Streaming algorithm, provides manageable and understandable views of the computational workload based on gLite reporting services. An online monitoring module shows the instant distribution of the jobs in real-time and its dynamics, enabling anomaly detection. An offline monitoring module provides the administratorwith a consolidated view of the workload, enabling the visual inspection of its long-term trends.
INDEX TERMS
Grid monitoring, Online clustering, Autonomic computing
CITATION

C. Germain-Renaud, M. Sebag and X. Zhang, "Multi-scale Real-Time Grid Monitoring with Job Stream Mining," Cluster Computing and the Grid, IEEE International Symposium on(CCGRID), Shanghai, China, 2009, pp. 420-427.
doi:10.1109/CCGRID.2009.20
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