Generic placeholder image

Current Medical Imaging

Author(s): Supriya Raheja*

DOI: 10.2174/1573405614666180903120708

DownloadDownload PDF Flyer Cite As
An Intuitionistic Fuzzy Based Novel Approach to CPU Scheduler

Page: [316 - 328] Pages: 13

  • * (Excluding Mailing and Handling)

Abstract

Background: The extension of CPU schedulers with fuzzy has been ascertained better because of its unique capability of handling imprecise information. Though, other generalized forms of fuzzy can be used which can further extend the performance of the scheduler.

Objectives: This paper introduces a novel approach to design an intuitionistic fuzzy inference system for CPU scheduler.

Methods: The proposed inference system is implemented with a priority scheduler. The proposed scheduler has the ability to dynamically handle the impreciseness of both priority and estimated execution time. It also makes the system adaptive based on the continuous feedback. The proposed scheduler is also capable enough to schedule the tasks according to dynamically generated priority. To demonstrate the performance of proposed scheduler, a simulation environment has been implemented and the performance of proposed scheduler is compared with the other three baseline schedulers (conventional priority scheduler, fuzzy based priority scheduler and vague based priority scheduler).

Results: Proposed scheduler is also compared with the shortest job first CPU scheduler as it is known to be an optimized solution for the schedulers.

Conclusion: Simulation results prove the effectiveness and efficiency of intuitionistic fuzzy based priority scheduler. Moreover, it provides optimised results as its results are comparable to the results of shortest job first.

Keywords: CPU scheduler, intuitionistic fuzzy set theory, intuitionistic fuzzy inference system, scheduling algorithm, priority scheduling algorithm, simulation.

Graphical Abstract

[1]
Silberschatz A, Galvin P, Gagne G. Operating system concepts. 9th ed. John Wiley & Sons 2012.
[2]
Stallings W. Operating systems internal and design principles. 8th ed. William Stallings 2014.
[3]
Kadhim JS, Al-Aubidy MK. Design and evaluation of a fuzzy-based CPU scheduling algorithm. Commun Comput Inf Sci 2010; 70: 45-52.
[http://dx.doi.org/10.1007/978-3-642-12214-9_9]
[4]
Lim S, Cho S, Intelligent OS. Process scheduling using fuzzy inference with user models. New trends in applied artificial intelligence. Lect Notes Comput Sci 2007; 45: 725-34.
[http://dx.doi.org/10.1007/978-3-540-73325-6_72]
[5]
Aburas A, Miho V. Fuzzy logic based algorithm for uniprocessor scheduling. In: Proceedings of the International Conference on Computer and Communication Engineering: Global Links for Human Development (ICCCE ’08). 2008 May 13-15; Kuala Lumpur, Malaysia IEEE 2008..
[http://dx.doi.org/10.1109/ICCCE.2008.4580654]
[6]
Alam B, Doja NM, Biswas R, et al. Fuzzy priority CPU scheduling algorithm. International IJCSI 2011; 6(1): 386-90.
[7]
Raheja S, Dhadich R, Rajpal S. Designing of 2-stage CPU scheduler using vague logic. Adv Fuzzy Syst 2014; 2014: 1-10.
[http://dx.doi.org/10.1155/2014/841976]
[8]
Tanenbaum SA, Woodfhull SA. Operating Systems Design And Implementation. 3rd ed. Prentice Hall 2006.
[9]
Behera HS, Sahu S, Bhoi KS. Weighted mean priority based scheduling for interactive systems. JGRCS 2011; 2(5): 1-6.
[10]
Nie B, Du J, Guoliang X, et al. A new operating system scheduling algorithm. Advanced Res Elect Comm Web Appl Comm 2011; 143: 92-6.
[http://dx.doi.org/10.1007/978-3-642-20367-1_15]
[11]
Xu J, Parnas LD. Priority scheduling versus pre-run-time scheduling. Int J Time-Critic Comp Syst 2000; 18: 7-23.
[12]
Zaim HA. Design of a scheduler: Comparison of different scheduling algorithms. J Electr Electron Eng (Oradea) 2003; 3: 859-77.
[13]
Park M, Yoo H, Chae J, et al. Quantum-based fixed priority scheduling. In: Proceedings of the International Conference on Advanced Computer Theory and Engineering. 2008 Dec 20-22; Phuket, Thailand IEEE 2009; pp.. 64-8.
[14]
Park M, Yoo H, Chae J. Integration of preemption threshold and quantum-based scheduling for schedulability enhancement of fixed priority tasks. In: 15th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications. 2009 Aug 24-26; Beijing, China IEEE 2009; pp. 503-10..
[http://dx.doi.org/10.1109/RTCSA.2009.60]
[15]
Oyetunji E, Oluleye A. Performance assessment of some CPU scheduling algorithms. Res J Inform Technol 2009; 1(1): 22-6.
[16]
Liu J, Lin K, Shih W, et al. Algorithms for scheduling imprecise computations. IEEE Computer 1991; 24: 58-68.
[http://dx.doi.org/10.1109/2.76287]
[17]
Zadeh AL. Fuzzy Sets. Inf Control 1965; 8: 338-56.
[http://dx.doi.org/10.1016/S0019-9958(65)90241-X]
[18]
Gau LW, Buehrer JD. Vague Sets. IEEE Trans Syst Man Cybern 1993; 23: 610-4.
[http://dx.doi.org/10.1109/21.229476]
[19]
Li D. Multiattribute Decision making models and methods using intuitionistic fuzzy sets. J Comput Syst Sci 2005; 70(1): 73-85.
[http://dx.doi.org/10.1016/j.jcss.2004.06.002]
[20]
De KS, Biswas R, Roy A. Application of intuitionistic fuzzy sets in medical diagnosis. Fuzzy Sets Syst 2001; 117(2): 209-13.
[http://dx.doi.org/10.1016/S0165-0114(98)00235-8]
[21]
Liu H, Wang G. Multi-criteria decision making methods based on intuitionistic fuzzy sets. Eur J Oper Res 2007; 179(1): 220-33.
[http://dx.doi.org/10.1016/j.ejor.2006.04.009]
[22]
Zadeh AL. Making computers think like people. IEEE Spectr 1984; 8: 26-32.
[http://dx.doi.org/10.1109/MSPEC.1984.6370431]
[23]
Zadeh AL. Is there a need for fuzzy logic? Inf Sci 2008; 178: 2751-79.
[http://dx.doi.org/10.1016/j.ins.2008.02.012]
[24]
Atanassov K. Intuitionistic fuzzy sets. Fuzzy Sets Syst 1986; 20: 87-96.
[http://dx.doi.org/10.1016/S0165-0114(86)80034-3]
[25]
Atanassov K. More on intuitionistic fuzzy sets. Fuzzy Sets Syst 1989; 33: 37-46.
[http://dx.doi.org/10.1016/0165-0114(89)90215-7]
[26]
Pawlak Z. Rough Sets. Int J Comp Inform Sci 1982; 1: 341-56.
[http://dx.doi.org/10.1007/BF01001956]
[27]
Lu A, Wilfred N. Vague sets or intuitionistic fuzzy sets for handling vague data: Which one is better? In: Lecture Notes in Computer Science. Springer 2005; pp. 401-16. In:
[28]
Bustince H, Burillo P. Vague Sets are intuitionistic Fuzzy Sets. Fuzzy Sets Syst 1996; 79: 403-5.
[http://dx.doi.org/10.1016/0165-0114(95)00154-9]
[29]
Atanassov K. New operations defined over the intuitionistic fuzzy sets. Fuzzy Sets Syst 1994; 61: 137-42.
[http://dx.doi.org/10.1016/0165-0114(94)90229-1]
[30]
Atanassov K. Operations over interval valued intuitionistic fuzzy sets. Fuzzy Sets Syst 1994; 64: 159-74.
[http://dx.doi.org/10.1016/0165-0114(94)90331-X]
[31]
Raheja S. Intuitionistic fuzzy set theory with fair share CPU scheduler: A dynamic approachtheoretical and practical advancements for fuzzy system integration. 1st ed. IGI Global 2017; pp. 126-53.
[32]
Raheja S, Dhadich R, Rajpal S. An Optimum time quantum using linguistic synthesis for round robin scheduling algorithm. Int J Soft Comput 2012; 3(1): 57-66.
[http://dx.doi.org/10.5121/ijsc.2012.3105]