A Practical approach on MLQ– fuzzy logic in...

A Practical approach on MLQ– fuzzy logic in CPU Scheduling

Author Name : Narender Singh, Dr. Yashpal Singh

ABSTRACT In this paper we propose using the scientific method to develop a deeper understanding of CPU schedulers; we use this approach to explain and understand the sometimes erratic behavior of CPU schedulers. Scheduling is a decision making process which deals with the allocation of resources to tasks over given time periods. Scheduling means how the processes can be assigned on the available CPU. It is a key feature in multitasking, multiprocessing and real-time operating system design. Scheduling is done by scheduler and dispatcher. A scheduler is a person or machine that organizes or maintains schedules. A dispatcher is a module which gives control of CPU to the process selected by the scheduler. Scheduling problem involves jobs that must schedule on machines subject to certain constraints to optimize some objective functions. There are number of CPU scheduling algorithms available, but it is very difficult task to decide which one is better. This thesis discusses the design and implementation of modified fuzzy based CPU scheduling algorithm. It demonstrates that scheduling done with new priority improves average waiting time and average turnaround time. This scheduling algorithm has two queues, one is at highest priority and execute tasks in round robin fashion while other is batch queue and executes in first in first out (FIFS) sequence. We will change these by fuzzy logic decisions which will update the priorities of task in second queue and time slot assigned in first queue. In this paper a new algorithm for the CPU scheduling is presented using FFGA (Fonseca and Fleming’s Genetic Algorithm) multi objective optimization. The performance of this algorithm was compared with seven classical scheduling algorithms (FCFS, RR (equal, prioritized), SJF (preemptive, non-preemptive, Priority (preemptive, non-preemptive)), and the results showed that the performance of the proposed method is more optimized than other methods. The proposed algorithm optimizes the average waiting time and response time for the processes.