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Multi-scale Real-time Grid Monitoring with Job Stream Mining

Xiangliang Zhang 1, 2 Michèle Sebag 1, 2 Cecile Germain-Renaud 1, 2, 3
1 TANC - Algorithmic number theory for cryptology
Inria Saclay - Ile de France, LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
2 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
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 GS T R AP system, embedding the S T R AP 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 administrator with a consolidated view of the workload, enabling the visual inspection of its long-term trends.
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Submitted on : Wednesday, March 11, 2009 - 4:19:04 PM
Last modification on : Thursday, July 8, 2021 - 3:48:15 AM
Long-term archiving on: : Friday, October 12, 2012 - 1:30:16 PM


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  • HAL Id : inria-00367544, version 1



Xiangliang Zhang, Michèle Sebag, Cecile Germain-Renaud. Multi-scale Real-time Grid Monitoring with Job Stream Mining. CCGrid, May 2009, Shangai, China. ⟨inria-00367544⟩



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