Neural Computing and Applications

, Volume 27, Issue 7, pp 1927–1939 | Cite as

A novel fuzzy decision-making system for CPU scheduling algorithm

  • Muhammad Arif Butt
  • Muhammad Akram
Original Article


In this research article, we present a novel fuzzy decision-making system to improve CPU scheduling algorithm of a multitasking operating system. We add intelligence to the existing scheduling algorithms by incorporating fuzzy techniques in the selection of a process to be run on CPU, which result in improved waiting and turn-around times. We implement our proposed algorithm as a simulator using C language. The simulator implements our fuzzy scheduling algorithm, reads the required parameters of all the ready to run processes from a file, and finally computes a dynamic priority (dpi) value for each process. The run queue is sorted according to each process’s dpi, and the process at the head of the queue is selected to run on CPU. Finally, we compare our results with some existing proposed fuzzy CPU scheduling (PFCS) algorithms as well as with some standard CPU schedulers. Our results show improvements as compared to the work of Ajmani’s PFCS (Ajmani and Sethi in BVICAM’s Int J Inf Technol 5:583–588, 2013), as well as from Behera’s improved fuzzy-based CPU scheduling algorithm (Behera et al. in Int J Soft Comput Eng 2:326–331, 2012). Our efforts contribute to the overall efforts of the community contributing to the fuzzification of different operating system modules. These efforts finally result in an operating system that gives convenience to its users in both certain and uncertain environments and at the same time efficiently utilize the underlying hardware and software under precise as well as fuzzy conditions (Kandel et al. in Fuzzy Sets Syst 99:241–251, 1988).


Operating system CPU scheduler Scheduling algorithms Fuzzy sets Fuzzy logic Fuzzy logic controller Defuzzification 



The authors are highly grateful to the referees for their invaluable comments and suggestions for improving the quality of our paper. The authors are also thankful to Professor Dr. Syed Mansoor Sarwar (Principal PUCIT).


  1. 1.
    Ajmani P, Sethi M (2013) Proposed fuzzy CPU scheduling algorithm (PFCS) for real time operating systems. BVICAM’s Int J Inf Technol 5:583–588Google Scholar
  2. 2.
    Akram M, Ashraf A, Sarwar M (2014) Novel applications of intuitionistic fuzzy digraphs in decision support systems. Sci World J, Article ID 904606, p 11. doi: 10.1155/2014/904606
  3. 3.
    Akram M, Shahzad S, Butt A, Khaliq A (2013) Intuitionistic fuzzy logic control for heater fans. Math Comput Sci 7:367–378CrossRefzbMATHGoogle Scholar
  4. 4.
    Alam B, Doja MN, Biswas R, Alam M (2011) Fuzzy priority CPU scheduling algorithm. Int J Comput Sci Issues 8:386–390Google Scholar
  5. 5.
    Ashraf A, Akram M, Sarwar M (2014) Fuzzy decision support system for fertilizer. Neural Comput Appl 25:1495–1505CrossRefGoogle Scholar
  6. 6.
    Ashraf A, Akram M, Sarwar M (2014) Type-II fuzzy decision support system for fertilizer. Sci World J, Article ID 695815Google Scholar
  7. 7.
    Behera HS, Pattanayak R, Mallick P (2012) An improved fuzzy-based CPU scheduling (IFCS) algorithm for real time systems. Int J Soft Comput Eng 2:326–331Google Scholar
  8. 8.
    Feng F, Akram M, Davvaz B, Fotea VL (2014) Attribute analysis of information systems based on elementary soft implications. Knowl Based Syst. Available on line. doi: 10.1016/j.knosys.2014.07.010
  9. 9.
    Gani AN (2012) A new operation on triangular fuzzy number for solving linear programming problem. Appl Math Sci 6:525–532MathSciNetzbMATHGoogle Scholar
  10. 10.
    Gottwald S (2005) Mathematical fuzzy logic as a tool for the treatment of vague information. Inf Sci 172:41–71MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Hamzeh M, Fakhraie SM, Lucas C (2007) Soft real time fuzzy task scheduling for multiprocessor systems. Int J Intell Technol 2:211–216Google Scholar
  12. 12.
    Kandel A, Zhang YQ, Henne M (1998) On use of fuzzy logic technology in operating systems. Fuzzy Sets Syst 99:241–251MathSciNetCrossRefGoogle Scholar
  13. 13.
    Leekwijck WV, Kerre E (1999) Defuzzification: criteria and classification. Fuzzy Sets Syst 108:159–178MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Lim S, Cho S (2007) Intelligent OS process scheduling using fuzzy inference with user models. In: Okuno HG, Ali M (eds) IEA/AIE, pp 725–734Google Scholar
  15. 15.
    Liu YJ, Tong SC, Chen CLP (2013) Adaptive fuzzy control via observer design for uncertain nonlinear systems with unmodeled dynamics. IEEE Trans Fuzzy Syst 21:275–288CrossRefGoogle Scholar
  16. 16.
    Qin-Li Z, Shi-Tong W (2009) Mamdani–Larsen fuzzy system based on expectation maximization algorithm and its applications to time series prediction. Acta Phys Sin 58:107–112Google Scholar
  17. 17.
    Regner T, Lacy C (2005) An introductory study of scheduling algorithms. CPSC 321: a project assignmentGoogle Scholar
  18. 18.
    Shen Q, Jiangc B, Cocquempot V (2013) Fuzzy logic system-based adaptive fault-tolerant control for near-space vehicle attitude dynamics with actuator faults. IEEE Trans Fuzzy Syst 21:301–313CrossRefGoogle Scholar
  19. 19.
    Silberschatz A, Galvin P, Gagne G (2008) Operating system concepts, 8th edition. Addison-Wesley, ISBN-10:0470128720Google Scholar
  20. 20.
    Tanaka SM (1991) Successive identification of a fuzzy modeand its application to prediction of a complex system. Fuzzy Sets Syst 42:315–334CrossRefzbMATHGoogle Scholar
  21. 21.
    Varshney PK, Akhtar N, Siddiqui MFH (2012) Efficient CPU scheduling algorithm using fuzzy logic. Int Conf Comput Technol Sci 47:13–18Google Scholar
  22. 22.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Zadeh LA (1975) The concept of linguistic variable and its application to approximate reasoning I. Inf Sci 8:199–249MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2015

Authors and Affiliations

  1. 1.Punjab University College of Information TechnologyUniversity of the PunjabLahorePakistan
  2. 2.Department of MathematicsUniversity of the PunjabLahorePakistan

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