N. Chabbah Sekma

Work place: National Engineering School of Tunis, University of Tunis El Manar, Tunis, 1002, Tunisia

E-mail: nahla.sekma@gmail.com


Research Interests: Computer Architecture and Organization, Data Structures and Algorithms, Combinatorial Optimization


Nahla Chabbah was born in Kalâa Kebira Tunisia in 1982. She received the Engineer and Master degrees from the University of Tunis El Manar (ENIT), Tunisia, in 2006 and 2008, respectively.

From 2006 until 2009, she was an Industrial Engineer in a subsidiary of the Dräxlmaier group in Tunisia. In 2009, she joined the Department of Computer Sciences at the University of Monastir (FSM), Tunisia, as a Contractual Assistant. Currently, she is a Ph.D. candidate in the Department of Industrial Engineering and a member of the OASIS research unit at the University of Tunis El Manar (ENIT), Tunisia. Her research interests are performance prediction, scheduling optimization and operational research.

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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