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Advances in Fuzzy Systems
Volume 2014 (2014), Article ID 841976, 10 pages
Research Article

Designing of 2-Stage CPU Scheduler Using Vague Logic

1Department of Computer Science & Engineering, ITM University, Gurgaon, India
2Department of Computer Science, University of Kota, Rajasthan, India
3Alpha Global IT, Toronto, ON, Canada

Received 15 January 2014; Revised 29 April 2014; Accepted 25 June 2014; Published 22 July 2014

Academic Editor: Adel M. Alimi

Copyright © 2014 Supriya Raheja et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In operating system the CPU scheduler is designed in such a way that all the resources are fully utilized. With static priority scheduling the scheduler ensures that CPU time will be assigned according to the highest priority but ignores other factors; hence it affects the performance. To improve the performance, we propose a new 2-stage vague logic based scheduler. In first stage, scheduler handles the uncertainty of tasks using the proposed vague inference system (VIS). In second stage, scheduler uses a vague oriented priority scheduling (VOPS) algorithm for selection of next process. The goal of this work is to handle the uncertainty as well as to optimize both the average and the amount of variation with respect to performance matrices average waiting time, average turnaround time, and average normalized turnaround time. A simulation using MATLAB is also conducted to evaluate the performance. Simulation results show that the proposed scheduler using VOPS algorithm is better than the scheduler with traditional priority scheduling algorithm. Results are based on the dual concept of fuzzy theory and its generalization, vague theory. Additionally, this work comprises the evaluation of VOPS and shortest job first algorithm. The outcome of proposed VOPS algorithm is much closer to the result obtained by traditional shortest job first.