A New Round Robin Algorithm for Task Scheduling in Real-time System

doi:10.22266/ijies2022.1031.59

Cited by 4 publications

(8 citation statements)

References 19 publications

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“…In order to demonstrate the efficiency of our technique and to ensure a fair comparison between the proposed algorithm and the RR, ADRR [9], EDRR [10], HYRR [11], MARR [12] and MMRR [13] algorithms, we select cases that are used in the majority of these algorithms and have the same number of processes, burst time, and arrival time. There are the datasets which consist of 15 processes that have been…”

Section: Resultsmentioning

confidence: 99%

“…In [13], the author proposed a Median Mean Round Robin (MMRR) approach that improves the functionality of the RR algorithm. The proposed approach determines an ideal dynamic time quantum for each round based on the remaining burst time of the processes, which is generated as (median+ mean)/2.…”

Section: Related Workmentioning

confidence: 99%

“…The processes are arriving at zero time with increasing burst time as shown in Table 1. A comparison of the RR, ADRR [9], EDRR [10], HYRR [11], MARR [12] and MMRR [13] algorithms with respect to TQ, AWT and ATT for case 2 is shown in Table 3 below.…”

Section: Case 2: Process Are Coming In Increasing Ordermentioning

confidence: 99%

“…A comparison of the RR, ADRR [9], EDRR [10], HYRR [11], MARR [12] and MMRR [13] algorithms with respect to TQ, AWT and ATT for case 3 is shown in Table 4 below. The comparison of AWT and ATT for the current algorithms is shown in Fig.…”

Section: Case 3: Process Are Coming In Decreasing Ordermentioning

confidence: 99%

“…The processes are ordered in ascending order and implemented to each process to reduce turnaround time, waiting time and number of context switches. The disadvantages of the presented algorithms, such as Amended Dynamic Round Robin (ADRR) [9], Efficient Dynamic Round Robin (EDRR) [10], Hybrid Round Robin (HYRR) [11], Median Average Round Robin (MARR) [12] and Median Mean Round Robin (MMRR) [13] are that they provide a higher AWT and ATT. The contribution of this proposed algorithm is to: 1-Decreasing AWT.…”

Section: Introductionmentioning

confidence: 99%

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MMMRR: a Modified Median Mean Round Robin Algorithm for Task Scheduling

2022

IJIES

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Real-time task scheduling is a critical part of systems since processes must finish their work at a certain time. Large data streams require high energy efficiency and rapid response times. Real-time system performance must be improved through task scheduling on resources. Numerous researchers have recently and rapidly proposed enormous efforts with enhancements in numerous task scheduling methods. Round Robin algorithm has been commonly utilized in task scheduling. In this paper, a Modified Median Mean Round Robin (MMMRR) algorithm is proposed to significantly enhance the performance of the Round Robin algorithm. The proposed algorithm finds an ideal dynamic time quantum └(median+ mean)/2)┘ and generated for each round depending on the remaining burst time of the processes. The system performance has been enhanced in terms of waiting time, turnaround time and context switching. The experimental results show that the proposed algorithm outperforms ADRR, HYRR, EDRR, MARR and MMRR algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Case 2: Process Are Coming In Increasing Ordermentioning

confidence: 99%

Section: Case 3: Process Are Coming In Decreasing Ordermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MMMRR: a Modified Median Mean Round Robin Algorithm for Task Scheduling

2022

IJIES

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RETRACTED: Dynamic round robin with adaptive time quanta (DRRATQ): A proposed algorithm

Hassan

2024

IFS

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The CPU scheduling technique influences the performance and efficiency of operating systems. Round-robin scheduling algorithm is ideal for time-shared systems, but it is not optimal for real-time operating systems since it yields more context switching, longer waiting time, and high turnaround time. The performance of the algorithm is predominantly influenced by the designated time quantum; however, determining a suitable time quantum is extremely challenging. This paper presents a CPU scheduling algorithm that provides a better tradeoff between waiting time, turnaround time, response time, and number of context switch by using hypothesis-based quanta generation approach. It combines the CPU burst requirements of actual processes with some noisy data and plots them against the presumed CPU quanta to get quanta densities so that a polynomial regression model can fit the data points with the highest adjusted R-squared. Then applying some complex inferential statistic, the required quanta is obtained. The scheduling is dynamic in nature because it generates the next CPU quanta in reference to the quanta that have been used in the previous cycle with remaining CPU burst requirements of the process, and it is also adaptive in nature because, at each cycle, it uses ‘d’ (5, 5, 4, 3, 2) degree of freedom to calculate the Jarque-Bera Statistics to accept/reject the hypothesis. The algorithm is implemented in ‘R’ and the performance has been evaluated on a sample size of five processes with some noisy data which outperforms the conventional RR and significantly reduces the performance parameters mentioned above. Implementing this algorithm to a time-sharing or distributed environment will undoubtedly improve system performance and will help to avoid issues like thrashing, incorporate aging, CPU affinity, and starvation. Since the proposed algorithm is work-conservative, therefore can be implemented in network packet switching, statistical multiplexing, and real-time systems.

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Optimizing Task Execution: The Impact of Dynamic Time Quantum and Priorities on Round Robin Scheduling

et al. 2023

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Task scheduling algorithms are crucial for optimizing the utilization of computing resources. This work proposes a unique approach for improving task execution in real-time systems using an enhanced Round Robin scheduling algorithm variant incorporating dynamic time quantum and priority. The proposed algorithm adjusts the time slice allocated to each task based on execution time and priority, resulting in more efficient resource utilization. We also prioritize higher-priority tasks and execute them as soon as they arrive in the ready queue, ensuring the timely completion of critical tasks. We evaluate the performance of our algorithm using a set of real-world tasks and compare it with traditional Round Robin scheduling. The results show that our proposed approach significantly improves task execution time and resource utilization compared to conventional Round Robin scheduling. Our approach offers a promising solution for optimizing task execution in real-time systems. The combination of dynamic time quantum and priorities adds a unique element to the existing literature in this field.

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A New Round Robin Algorithm for Task Scheduling in Real-time System

Cited by 4 publications

References 19 publications

MMMRR: a Modified Median Mean Round Robin Algorithm for Task Scheduling

MMMRR: a Modified Median Mean Round Robin Algorithm for Task Scheduling

RETRACTED: Dynamic round robin with adaptive time quanta (DRRATQ): A proposed algorithm

Optimizing Task Execution: The Impact of Dynamic Time Quantum and Priorities on Round Robin Scheduling

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