An Evaluation of Linear Models for Host Load Prediction
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
Homeostatic and Tendency-Based CPU Load Predictions
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Performance prediction and its use in parallel and distributed computing systems
Future Generation Computer Systems - Systems performance analysis and evaluation
Proceedings of the 2007 workshop on Grid monitoring
Genetic algorithms as global random search methods: An alternative perspective
Evolutionary Computation
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Automatic Control of Distributed Systems Based on State Prediction Methods
CISIS '10 Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems
Decomposition Based Algorithm for State Prediction in Large Scale Distributed Systems
ISPDC '10 Proceedings of the 2010 Ninth International Symposium on Parallel and Distributed Computing
Identification and control of dynamical systems using the self-organizing map
IEEE Transactions on Neural Networks
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The state prediction of resources in large scale distributed systems represents an important aspect for resources allocations, systems evaluation, and autonomic control. The paper presents advanced techniques for resources state prediction in Large Scale Distributed Systems, which include techniques based on bio-inspired algorithms like neural network improved with genetic algorithms. The approach adopted in this paper consists of a new fitness function, having prediction error minimization as the main scope. The proposed prediction techniques are based on monitoring data, aggregated in a history database. The experimental scenarios consider the ALICE experiment, active at the CERN institute. Compared with classical predicted algorithms based on average or random methods, the authors obtain an improved prediction error of 73%. This improvement is important for functionalities and performance of resource management systems in large scale distributed systems in the case of remote control ore advance reservation and allocation.