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Authors: Omar AlHeyasat and Randa Herzallah

Affiliation: Al-Balqa' Applied University, Jordan

Keyword(s): Round-robin Scheduling Algorithm, Neural Networks Model, Quantum Time, Length Estimation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computational Neuroscience ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Theory and Methods

Abstract: The quantum time length is usually taken as a fixed value in all applications that use Round Robin (RR) scheduling algorithm. The determination of the optimal length of the quantum that results in a small average turn around time is very complicated because of the unknown nature of the tasks in the ready queue. The round robin algorithm becomes very similar to the first in first served algorithm if the quantum length is large. On the other hand, high context switch results for small values of quantum length which might cause central processing unit (CPU) thrashing. In this paper we propose a new RR scheduling algorithm based on using neural network models for predicting the optimal quantum length that yields minimum average turn around time. The quantum length is taken to be a function of the service time of the various jobs available in the ready queue. This in contrast to the traditional methods of using fixed quantum length is shown to give better results and to minimize the aver age turnaround time for almost any collection of jobs in the ready queue. (More)

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Paper citation in several formats:
AlHeyasat, O. and Herzallah, R. (2010). ESTIMATION OF QUANTUM TIME LENGTH FOR ROUND-ROBIN SCHEDULING ALGORITHM USING NEURAL NETWORKS . In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (IJCCI 2010) - ICNC; ISBN 978-989-8425-32-4, SciTePress, pages 253-257. DOI: 10.5220/0003058002530257

@conference{icnc10,
author={Omar AlHeyasat. and Randa Herzallah.},
title={ESTIMATION OF QUANTUM TIME LENGTH FOR ROUND-ROBIN SCHEDULING ALGORITHM USING NEURAL NETWORKS },
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (IJCCI 2010) - ICNC},
year={2010},
pages={253-257},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003058002530257},
isbn={978-989-8425-32-4},
}

TY - CONF

JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (IJCCI 2010) - ICNC
TI - ESTIMATION OF QUANTUM TIME LENGTH FOR ROUND-ROBIN SCHEDULING ALGORITHM USING NEURAL NETWORKS
SN - 978-989-8425-32-4
AU - AlHeyasat, O.
AU - Herzallah, R.
PY - 2010
SP - 253
EP - 257
DO - 10.5220/0003058002530257
PB - SciTePress

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