Decomposition Based Algorithm for State Prediction in Large Scale Distributed Systems

Prediction represents an important component of resource management, providing information about the future state, utilization and availability of resources. We propose a new prediction algorithm inspired from the decomposition of a complex wave into simpler waves with fixed frequencies (similar to Fourier decomposition). The partial results obtained from this decomposition stage are combined using approaches inspired from artificial intelligence models. The experimental results for different system parameters, used in Alice experiment, highlight the great improvement, discussed in terms of error reduction, offered by this new prediction algorithm. The tests were made using real-time monitoring data provided by a system monitoring tool, in the case of one-step and multi-step ahead prediction. The prediction's results can be used by the resource management systems in order to improve the scheduling decisions, assuring the load balancing and optimizing the resource utilization.

[1]  Aluizio F. R. Araújo,et al.  Identification and control of dynamical systems using the self-organizing map , 2004, IEEE Transactions on Neural Networks.

[2]  Rodrigo Fernandes de Mello,et al.  A novel approach for distributed application scheduling based on prediction of communication events , 2010, Future Gener. Comput. Syst..

[3]  Catalin Cirstoiu,et al.  Monitoring, accounting and automated decision support for the alice experiment based on the MonALISA framework , 2007, GMW '07.

[4]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[5]  Peter A. Dinda,et al.  An evaluation of linear models for host load prediction , 1999, Proceedings. The Eighth International Symposium on High Performance Distributed Computing (Cat. No.99TH8469).

[6]  Valentin Cristea,et al.  Automatic Control of Distributed Systems Based on State Prediction Methods , 2010, 2010 International Conference on Complex, Intelligent and Software Intensive Systems.

[7]  Mojtaba Ahmadieh Khanesar,et al.  Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods , 2009, Appl. Soft Comput..

[8]  Iosif Legrand,et al.  Monitoring and control of large systems with MonALISA , 2009, CACM.

[9]  Philippe Preux,et al.  Basis Function Construction in Reinforcement Learning Using Cascade-Correlation Learning Architecture , 2008, 2008 Seventh International Conference on Machine Learning and Applications.