PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multi-scale Realtime Grid Monitoring with Job Stream Mining
Xiangliang Zhang, Michele Sebag and Cecile Germain
In: 9th IEEE International Symposium on Cluster Computing and the Grid (CCGrid'09), 18 -21 May 2009, Shanghai, China.

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 G-StrAP 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 administrator with a consolidated view of the workload, enabling the visual inspection of its long-term trends.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4497
Deposited By:Xiangliang Zhang
Deposited On:13 March 2009