An Efficient Fault-Tolerant Scheduling Approach with Energy Minimization for Hard Real-Time Embedded Systems

Kada, Barkahoum; Kalla, Hamoudi

doi:10.1007/978-3-030-40131-3_7

Communications in Computer and Information Science

2020

DOI: 10.1007/978-3-030-40131-3_7

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An Efficient Fault-Tolerant Scheduling Approach with Energy Minimization for Hard Real-Time Embedded Systems

Barkahoum Kada¹,

Hamoudi Kalla²

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Cited by 2 publications

References 27 publications

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Maintaining the completion-time mechanism for Greening tasks scheduling on DVFS-enabled computing platforms

Hagras,

El-Sayed

2024

Cluster Comput

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The key factor in reducing the consumed energy when dependent-tasks applications are scheduled on DVFS-enabled computing platforms is task execution time slots. The unique and axiomatic approach to reduce the energy consumption on such platforms involves scaling down the execution frequency of each task within its execution time slot, provided a suitable scaling-down frequency is available. Regrettably, scheduling algorithms often shrink task execution time slots due to minimizing task completion times. This paper presents BlueMoon, a mechanism that reschedules the application tasks to extend the execution time slot of each task while ensuring that the overall completion time of the application tasks remains unaffected. BlueMoon is implemented and tested on numerous schedules of application graphs. The experimental results, conducted through computer simulations, demonstrate that BlueMoon substantially extends the execution time slots of tasks when compared to other mechanisms.

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Maintaining the completion-time mechanism for Greening tasks scheduling on DVFS-enabled computing platforms

Hagras,

El-Sayed

2024

Cluster Comput

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Uncertainty Aware T2SS Based Dyna-Q-Learning Framework for Task Scheduling in Grid Computing

Bhargavi

Shiva

2022

Cybernetics and Information Technologies

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Task scheduling is an important activity in parallel and distributed computing environment like grid because the performance depends on it. Task scheduling gets affected by behavioral and primary uncertainties. Behavioral uncertainty arises due to variability in the workload characteristics, size of data and dynamic partitioning of applications. Primary uncertainty arises due to variability in data handling capabilities, processor context switching and interplay between the computation intensive applications. In this paper behavioral uncertainty and primary uncertainty with respect to tasks and resources parameters are managed using Type-2-Soft-Set (T2SS) theory. Dyna-Q-Learning task scheduling technique is designed over the uncertainty free tasks and resource parameters. The results obtained are further validated through simulation using GridSim simulator. The performance is good based on metrics such as learning rate, accuracy, execution time and resource utilization rate.

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An Efficient Fault-Tolerant Scheduling Approach with Energy Minimization for Hard Real-Time Embedded Systems

Cited by 2 publications

References 27 publications

Maintaining the completion-time mechanism for Greening tasks scheduling on DVFS-enabled computing platforms

Maintaining the completion-time mechanism for Greening tasks scheduling on DVFS-enabled computing platforms

Uncertainty Aware T2SS Based Dyna-Q-Learning Framework for Task Scheduling in Grid Computing

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