An Intuitionistic Fuzzy Based Novel Approach to CPU Scheduler

Area of Science:

• Computer Science
• Artificial Intelligence

Background:

• Fuzzy logic enhances CPU schedulers by handling imprecise information.
• Generalized fuzzy forms can further improve scheduler performance.

Purpose of the Study:

• Introduce a novel intuitionistic fuzzy inference system for CPU scheduling.
• Design a CPU scheduler that dynamically adapts to imprecise data.

Main Methods:

• Implemented an intuitionistic fuzzy inference system within a priority scheduler.
• Enabled dynamic handling of priority and estimated execution time.
• Compared performance against conventional, fuzzy, vague, and shortest job first schedulers via simulation.

Main Results:

• The intuitionistic fuzzy scheduler effectively manages dynamic impreciseness.
• Achieved adaptive scheduling based on continuous feedback.
• Demonstrated optimized performance comparable to shortest job first.

Conclusions:

• The intuitionistic fuzzy-based priority scheduler is effective and efficient.
• Outperforms baseline schedulers and achieves results competitive with optimized solutions.