A. Elleuch

Work place: National School of Computer Sciences, University of Manouba, Manouba, 2010, Tunisia

E-mail: ahmed.elleuch@ensi.rnu.tn


Research Interests: Planning and Scheduling


Ahmed Elleuch was born in Tunis Tunisia in 1966. He received an Engineer degree from the University of Tunis, Tunisia in 1990, the DEA and Ph.D degrees in Computer Science, both from the Institut National Polytechnique de Grenoble in France in 1991 and 1994, respectively.

Since 1995, he is an Assistant Professor and a member of CRISTAL Laboratory at the University of Manouba (ENSI), Tunisia. His current research interests are in the field of middleware for grid, cloud, peer-to-peer and large scale systems, with a special focus on optimizing task scheduling, load balancing and using economic incentive models for such systems.

Author Articles
Automated Forecasting Approach Minimizing Prediction Errors of CPU Availability in Distributed Computing Systems

By N. Chabbah Sekma A. Elleuch N. Dridi

DOI: https://doi.org/10.5815/ijisa.2016.09.02, Pub. Date: 8 Sep. 2016

Forecasting CPU availability in volunteer computing systems using a single prediction algorithm is insufficient due to the diversity of the world-wide distributed resources. In this paper, we draw-up the main guidelines to develop an appropriate CPU availability prediction system for such computing infrastructures. To reduce solution time and to enhance precision, we use simple prediction techniques, precisely vector autoregressive models and a tendency-based technique. We propose a predictor construction process which automatically checks assumptions of vector autoregressive models in time series. Three different past analyses are performed. For a given volunteer resource, the proposed prediction system selects the appropriate predictor using the multi-state based prediction technique. Then, it uses the selected predictor to forecast CPU availability indicators. We evaluated our prediction system using real traces of more than 226000 hosts of Seti@home. We found that the proposed prediction system improves the prediction accuracy by around 24%.

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