A New Intuitionistic Fuzzy Mamdani Inference System for Priority and Starvation in Scheduling Policy
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Abstract
Process scheduling algorithms make multiprogramming more productive by optimizing the utilization of the CPU. It is a very crucial task of scheduling algorithms that the CPU should not ideal if processes are in ready queue. We developed a unique algorithm for improving the process execution in real-time systems by utilizing the modified round robin (RR) algorithm variations that utilizes the use of the degree of multiprogramming. The developed algorithm removes the issue of starvation as compared with traditional priority scheduling algorithms. We also proposed an intuitionistic fuzzy inference system based MMFNP model to calculate the new priority that plays a key role for the execution of urgent tasks. Maximum value of New Priority represents the uppermost priority. The proposed algorithm increases the throughput of the system and reduces the average response time, average waiting time and average turnaround time. For the performance evaluation of the suggested algorithm, we also compared the results with RR algorithm and priority-based RR algorithm.
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