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
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.