Issue No.01 - January (2012 vol.23)
Liang Hu , Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2011.108
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.
software architecture, grid computing, learning (artificial intelligence), resource allocation, scheduling, system architecture evaluation, online system, grid resource monitoring, machine learning-based prediction, resource allocation, job scheduling, grid computing, distributed system, Monitoring, Predictive models, Information services, Registers, Data models, Computer architecture, Containers, particle swarm optimization., Grid resource, monitoring and prediction, neural network, support vector machine, genetic algorithm
Liang Hu, "Online System for Grid Resource Monitoring and Machine Learning-Based Prediction", IEEE Transactions on Parallel & Distributed Systems, vol.23, no. 1, pp. 134-145, January 2012, doi:10.1109/TPDS.2011.108