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Digital Library of the
European Council for Modelling and Simulation |
Title: |
Using Artificial Neural Network For Monitoring And Supporting The Grid
Scheduler Performance |
Authors: |
Daniel Grzonka, Joanna Kolodziej, Jie Tao |
Published in: |
(2014).ECMS 2014 Proceedings edited
by: Flaminio Squazzoni,
Fabio Baronio, Claudia Archetti,
Marco Castellani European Council for
Modeling and Simulation. doi:10.7148/2014 ISBN:
978-0-9564944-8-1 28th
European Conference on Modelling and Simulation, Brescia,
Italy, May 27th – 30th,
2014 |
Citation
format: |
Daniel
Grzonka, Joanna Kolodziej,
Jie Tao (2014). Using
Artificial Neural Network For Monitoring And Supporting The Grid Scheduler
Performance, ECMS 2014 Proceedings edited by: Flaminio
Squazzoni, Fabio Baronio,
Claudia Archetti, Marco Castellani European
Council for Modeling and Simulation. doi:10.7148/2014-0515 |
DOI: |
http://dx.doi.org/10.7148/2014-0515 |
Abstract: |
Task scheduling and resource
allocations are the key issues for computational grids. Distributed resources
usually work at different autonomous domains with their own access and
security policies that impact successful job executions across the domain
boundaries. In this paper, we propose an Artificial Neural Network (ANN)
approach for supporting the security awareness of evolutionary driven grid
schedulers. Making a prior analysis of the trust levels of resources and
security demand parameters of tasks, the neural network monitors the
scheduling and task execution processes. In the result produce the
tasks-machines mapping “suggestions”, which can be then utilized by the scheduler
to reduce the makespan or increase the system throughput.
In this paper, we report the development of risk-resilient genetic-based
schedulers and their integration with an ANN module of the HyperSim-G Grid Simulator to evaluate the proposed model
under the heterogeneity and large-scale system dynamics. The simulation
results showed a significant impact of the ANN support on enhancing the
effectiveness of the genetic-based meta-heuristics in reducing the cost of security
awareness in grid scheduling. |
Full
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