The extension of CPU schedulers with fuzzy logic has been ascertained better because of its capability of handling imprecise information. Though, other generalized forms of fuzzy logic can be used which can further extend the performance of the scheduler. This paper introduces a novel approach for Highest Response Ratio Next (HRRN) CPU scheduler which uses the concept of intuitionistic fuzzy set theory. The proposed scheduler has the capability to dynamically deal with the impreciseness of the response ratio and to make the system adaptive based on the continuous feedback. An intuitionistic fuzzy inference system has been implemented. In this paper, to validate the performance of the scheduler a simulation environment has been implemented and the performance is compared with the other three baseline schedulers (Conventional HRRN scheduler, Fuzzy based HRRN scheduler and Vague based HRRN scheduler). Simulation results prove the effectiveness and efficiency of intuitionistic fuzzy based HRRN scheduler.
SilberschatzAGalvinPBGagneG. Operating System Concepts, John Wiley & Sons, 9th Edition, 2012.
2.
StallingsW. Operating Systems Internal and Design Principles, William Stallings, 8th Edition, 2014.
3.
RahejaS. Designing of vague logic based 2-layered framework for CPU scheduler. Advances in Fuzzy Systems.2016; 16: 1-11.
4.
ZaimAH. Design of a scheduler: Comparison of different scheduling algorithms. Journal of Electrical & Electronics Engineering.2003; 3: 859-877.
5.
Panduranga RaoMVShetKC. Analysis of new multi-level feedback queue scheduler for real time kernel. International Journal of Computational Cognition.2010; 8: 5-16.
6.
BlevinsPRRama MoorthyCV. Aspects of a dynamically adaptive operating systems. IEEE Transactions on Computers.1976; 25: 713-725.
7.
AndrewSTanenbaumAWoodfhullS. Operating Systems Design and Implementation, Prentice Hall, Third Edition, 2006.
8.
RahejaSDhadichRRajpalS. Vague oriented highest response ratio next scheduling algorithm. IEEE International Conference on Fuzzy Systems (Fuzz-2013), 2013, pp. 1-7.
9.
MoallemiAAsgharilarimiM. A fuzzy scheduling algorithm based on highest response ratio next algorithm, K. Elleithy (ed.), Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering, Springer, 2008, pp. 75-80.
10.
AlamBDojaMNBiswasRAlamM. Fuzzy HRRN CPU Scheduling Algorithm. International Journal of Computer Science and Information Security. 2011; 9: 120-124.
11.
RahejaSDhadichRRajpalS. 2-layered architecture of vague logic based multilevel queue scheduler. Applied Compuational Intelligence and Soft Computing. 2014; 2014: 1-12.
12.
ZadehLA. Fuzzy sets, Information and Control. 1965; 8: 338-356.
13.
AtanassovK. Intuitionistic fuzzy sets. Fuzzy Sets and Systems.1986; 20: 87-96.
14.
AtanassovK. More on intuitionistic fuzzy sets. Fuzzy Sets Systems.1989; 33: 37-46.
15.
GauWLBuehrerDJ. Vague sets. IEEE Transaction on Systems, Man and Cybernetics.1993; 23: 610-614.
16.
AtanassovK. Intuitionistic Fuzzy Sets: Theory and Applications, Studies in Fuzziness and Soft Computing, Physica-Verlag, New York, 2000. 35.
BustinceHBurilloP. Vague sets are intuitionistic fuzzy sets. Fuzzy sets and systems.1996; 79: 403-405.
19.
AnLWilfredN. Vague sets or intuitionistic fuzzy sets for handling vague data: Which one is better? Lecture Notes in Computer Science, Springer.2005; 3916: 401-416.
20.
ZainJMMohdWMBWEl-QawasmehE. Highest Response Ratio Next (HRRN) vs. First Come First Serve (FCFS) scheduling Algorithm in Grid Environment, Software Engineering and Computer Systems – Second International Conference, June 27–29 2011.
21.
Surendra VarmaP. Design of modified HRRN scheduling algorithm for priority systems using hybrid priority scheme. Journal of Telematics and Informatics.2013; 1: 14-19.
22.
NieBDuJGuoliangXLiuHYuRWenQ. A new operating system scheduling algorithm. Advanced Research on Electronic Commerce, Web Application, and Communication, Springer.2011; 143: 92-96.
23.
JaneWLiuSLinKJShihWKYuCA. Algorithms for scheduling imprecise computations. IEEE Computer.1991; 24: 58-68.
24.
LimSChoSB. Intelligent OS process scheduling using fuzzy inference with user models. New Trends in Applied Artificial Intelligence, Lecture Notes in Computer Science.2007; 45: 725-734.
25.
Aburas AbdurazzagAVladimirM. Fuzzy logic based algorithm for uniprocessor scheduling, Proceedings of the International Conference on Computer and Communication Engineering, 2008. pp. 499-504.
26.
ZadehLA. Making computers think like people. IEEE Spectrum.1984; 8: 26-32.
27.
ZadehLA. Is there a need for Fuzzy Logic. Information Science.2008; 178: 2751-2779.
28.
AtanassovK. New operations defined over the intuitionistic fuzzy sets. Fuzzy Sets Systems.1994; 61: 137-142.
29.
PawlakZ. Rough Sets. International Journal of Computer and Information Sciences. 1982; 11: 341-356.
RahejaS. Intuitionistic Fuzzy Set Theory with Fair Share CPU Scheduler: A Dynamic Approach in Theoretical and Practical Advancements for Fuzzy System Integration, 2017, pp. 126-153.
32.
ZanjiraniDMEsmaelianM. An integrated approach based on Fuzzy Inference System for scheduling and process planning through multiple objectives, American Institute of Mathematical Sciences, Dec 2018.
33.
RahejaSJainV. Designing of a new intuitionistic fuzzy based diabetic diagnostic system. International Journal of Fuzzy System Applications (IJFSA).2018; 7: 32-45.
34.
PauziaHMAbdullahL. Implementation of intuitionistic fuzzy inference systems to assess air quality forecast: Case of Malaysia, AIP Conference Proceedings, 1974, June 2018.
35.
MohammedBMostafaA. Task parameters impacts in fuzzy real time scheduling in recent advances in intuitionistic fuzzy logic system, 2018.
36.
RahejaS. An intuitionistic fuzzy based novel approach to CPU scheduler. Current Medical Imaging.2019; 14.
