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A proposed solution for the load balancing problem on heterogeneous clusters based on a delayed neural network

A proposed solution for the load balancing problem on heterogeneous clusters based on a delayed... Purpose – The purpose of this paper is to analyze the load balancing (LB) problem in clusters of heterogeneous processors using delayed artificial neural networks theory, optimal control theory, and linear matrix inequalities (LMIs). Design/methodology/approach – Starting with a mathematical model that includes delays and processors with different processing velocities, this model is transformed into a special case of a neural network model known as delayed cellular neural network (DCNN) model. A new energy function is proposed to this delayed neural network special case, assuring convergence conditions through the use of LMIs. Some performance criteria subject to stability conditions to the non‐linear model version are analyzed, and a new LB controller systematic method of synthesis is proposed, using two coupled LMIs – one guaranteeing global convergence and the other guaranteeing performance in a linear region of operation. Simulations and experiments proves the efficiency of this approach, reducing LB time with a viable computational cost for clusters with high number of processors. Findings – A new approach for the LB problem was proposed based on an special case of a delayed neural network model. Performance criterium can also be imposed over it using a quadratic cost function, giving a possibility to extend the idea to other classes of delayed neural network. Originality/value – The novelty associated with this paper is the introduction of an approach which the LB problem on an heterogeneous cluster of local processors can be modeled as a delayed neural network and the performance of the LB algorithm can be imposed, at least locally, by a quadratic cost function. Also, the delayed neural network can also be seen as a Persidskii system with delay. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Computing and Cybernetics Emerald Publishing

A proposed solution for the load balancing problem on heterogeneous clusters based on a delayed neural network

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Publisher
Emerald Publishing
Copyright
Copyright © 2010 Emerald Group Publishing Limited. All rights reserved.
ISSN
1756-378X
DOI
10.1108/17563781011028550
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to analyze the load balancing (LB) problem in clusters of heterogeneous processors using delayed artificial neural networks theory, optimal control theory, and linear matrix inequalities (LMIs). Design/methodology/approach – Starting with a mathematical model that includes delays and processors with different processing velocities, this model is transformed into a special case of a neural network model known as delayed cellular neural network (DCNN) model. A new energy function is proposed to this delayed neural network special case, assuring convergence conditions through the use of LMIs. Some performance criteria subject to stability conditions to the non‐linear model version are analyzed, and a new LB controller systematic method of synthesis is proposed, using two coupled LMIs – one guaranteeing global convergence and the other guaranteeing performance in a linear region of operation. Simulations and experiments proves the efficiency of this approach, reducing LB time with a viable computational cost for clusters with high number of processors. Findings – A new approach for the LB problem was proposed based on an special case of a delayed neural network model. Performance criterium can also be imposed over it using a quadratic cost function, giving a possibility to extend the idea to other classes of delayed neural network. Originality/value – The novelty associated with this paper is the introduction of an approach which the LB problem on an heterogeneous cluster of local processors can be modeled as a delayed neural network and the performance of the LB algorithm can be imposed, at least locally, by a quadratic cost function. Also, the delayed neural network can also be seen as a Persidskii system with delay.

Journal

International Journal of Intelligent Computing and CyberneticsEmerald Publishing

Published: Mar 30, 2010

Keywords: Neural nets; Control theory; Program processors; Loading (physics); Cluster analysis

References