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A smart intuitionistic fuzzy-based framework for round-robin short-term scheduler

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A Correction to this article was published on 11 October 2021

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Abstract

A smart intuitionistic fuzzy-based framework is designed to facilitate adaptability by providing continuous changes in the size of time slice to scheduler at run time. The present work models a round-robin scheduler with its imprecise parameters. To manage the impreciseness among parameters and to improve the performance, an intuitionistic fuzzy-based round-robin scheduler is implemented. IFRR scheduler integrates the two components, namely intuitionistic fuzzy inference system and hybrid round-robin scheduling approach. Intuitionistic fuzzy inference system is implemented to handle the impreciseness of burst time to provide a dynamic time slice to scheduler, whereas hybrid round-robin scheduling approach is used to make a decision on selection of next task to run. The prove the performance, the proposed scheduler is compared with the other baseline round-robin schedulers. The results prove the efficiency of scheduler in terms of average waiting time, average turnaround time, average normalized turnaround time, and number of context switches.

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Funding

Dr. Ahmed A. Mohamed extends his appreciation to the Deanship of scientific research at Majmaah University for funding this work under project no. R-2021-200.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amity University, Noida, India

    Supriya Raheja

  2. Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Majmaah, Majmaah, 11952, Saudi Arabia

    Mohammed Alshehri

  3. Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia

    Ahmed A. Mohamed

  4. Department of Information Technology, Faculty of Computer and Information, Assiut University, Assiut, 71515, Egypt

    Ahmed A. Mohamed

  5. Department of Computer Science, Galgotia University, Greater Noida, India

    Supriya Khaitan

  6. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India

    Manoj Kumar

  7. Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, 560054, India

    Thompson Stephan

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  1. Supriya Raheja
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  2. Mohammed Alshehri
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  4. Supriya Khaitan
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  5. Manoj Kumar
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  6. Thompson Stephan
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Correspondence to Mohammed Alshehri.

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The original online version of this article was revised: In the original version of this article, the given and family names of Stephen Thompson were incorrectly structured.

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Raheja, S., Alshehri, M., Mohamed, A.A. et al. A smart intuitionistic fuzzy-based framework for round-robin short-term scheduler. J Supercomput 78, 4655–4679 (2022). https://doi.org/10.1007/s11227-021-04052-4

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