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

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.