Online System for Grid Resource Monitoring and Machine Learning-Based Prediction

Resource allocation and job scheduling are the core functions of grid computing. These functions are based on adequate information of available resources. Timely acquiring resource status information is of great importance in ensuring overall performance of grid computing. This work aims at building...

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Veröffentlicht in:IEEE transactions on parallel and distributed systems 2012-01, Vol.23 (1), p.134-145
Hauptverfasser: Hu, Liang, Che, Xi-Long, Zheng, Si-Qing
Format: Artikel
Sprache:eng
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Zusammenfassung:Resource allocation and job scheduling are the core functions of grid computing. These functions are based on adequate information of available resources. Timely acquiring resource status information is of great importance in ensuring overall performance of grid computing. This work aims at building a distributed system for grid resource monitoring and prediction. In this paper, we present the design and evaluation of a system architecture for grid resource monitoring and prediction. We discuss the key issues for system implementation, including machine learning-based methodologies for modeling and optimization of resource prediction models. Evaluations are performed on a prototype system. Our experimental results indicate that the efficiency and accuracy of our system meet the demand of online system for grid resource monitoring and prediction.
ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2011.108