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

Raheja, Supriya; Alshehri, Mohammed; Mohamed, Ahmed A.; Khaitan, Supriya; Kumar, Manoj; Stephan, Thompson

doi:10.1007/s11227-021-04052-4

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…This approach employed an intuitionistic fuzzy inference system and a hybrid Round Robin strategy to adjust the time slice dynamically for inaccurate burst time. On the other hand, the hybrid Round Robin approach determines the task selection process [2].…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

“…The process is then allocated memory and system resources and added to the scheduler's queue. The scheduler decides which method to execute next based on the scheduling algorithm [1][2][3][4]. Therefore, various scheduling algorithms are proposed to address this problem, each with its trade-offs and advantages.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…Time series prediction is widely used in financial markets, 1 meteorological phenomena, 2 engineering technology, 3,4 pandemic prevention, 5,6 and other fields 7,8 . Generally, methods for time series analysis can be divided into two categories: time domain analysis 9 and frequency domain analysis 10 …”

Section: Introductionmentioning

confidence: 99%

Dual‐stage time series analysis on multifeature adaptive frequency domain modeling

Liu

Lin

Liu

et al. 2022

Int J of Intelligent Sys

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Time series research in academic and industrial fields has attracted wide attention. However, the frequency information contained in time series still lacks effective modeling. The studies found that time series forecasting relies on different frequency patterns: short-term series forecasting relies more on high-frequency components, while long-term forecasting focuses more on low-frequency data. To better describe the multifrequency mode, a dual-stage multifeature adaptive frequency domain prediction model (DMAFD) is proposed in this paper. DMAFD contains two stages. First, it adopts the XGBoost algorithm to obtain a feature vector by analyzing the feature importance. Second, the frequency feature extraction of time series and the frequency aware modeling of the target sequence is integrated, for building an end-to-end prediction network based on the dependence of time series on frequency mode. The innovation is reflected in the fact that the prediction network can automatically focus on multifrequency components according to the dynamic evolution of the input sequence. Extensive experiments on four real data sets from different fields show that DMAFD obtains higher accuracy and smaller lags in time step analysis compared with state-of-the-art algorithms.

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DeepSafety: a deep neural network-based edge computing framework for detecting unsafe behaviors of construction workers

Zhang

Liu²,

Ying³

2023

J Ambient Intell Human Comput

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

Cited by 11 publications

References 27 publications

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

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

Dual‐stage time series analysis on multifeature adaptive frequency domain modeling

DeepSafety: a deep neural network-based edge computing framework for detecting unsafe behaviors of construction workers

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