2019
DOI: 10.52549/ijeei.v7i2.464
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Improved time quantum length estimation for round robin scheduling algorithm using neural network

Abstract: In most cases, the quantum time length is taken to be fix in all applications that use Round Robin (RR) scheduling algorithm. Many attempts aim to determination of the optimal length of the quantum that results in a small average turnaround time, but the unknown nature of the tasks in the ready queue make the problem more complicated: Considering a large quantum length makes the RR algorithm behave like a First Come First Served (FIFO) scheduling algorithm, and a small quantum length cause high number of conte… Show more

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Cited by 3 publications
(1 citation statement)
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“…Modifying the time slice at the beginning of each round is another dynamic version of SRR proposed by Lipika [24], in which the time slice is calculated with respect to the residual burst times (RBT) in the successive rounds. Lipika benefited from SJF in which the processes are ordered increasingly based on their burst times (i.e., the process with the highest burst time will be at the tail of the ready queue and the process with the lowest burst time will be at the head of the queue) [25][26][27]. Adaptive80 RR is a dynamic variant of SRR proposed by Christoph and Jeonghw [6].…”
Section: Related Workmentioning
confidence: 99%
“…Modifying the time slice at the beginning of each round is another dynamic version of SRR proposed by Lipika [24], in which the time slice is calculated with respect to the residual burst times (RBT) in the successive rounds. Lipika benefited from SJF in which the processes are ordered increasingly based on their burst times (i.e., the process with the highest burst time will be at the tail of the ready queue and the process with the lowest burst time will be at the head of the queue) [25][26][27]. Adaptive80 RR is a dynamic variant of SRR proposed by Christoph and Jeonghw [6].…”
Section: Related Workmentioning
confidence: 99%