<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

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

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Task scheduling is an important activity in parallel and distributed computing environment like grid because the performance depends on it. Task scheduling...

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

Publié en ligne: 22 sept. 2022

Pages: 48 - 67

Reçu: 25 mars 2022

Accepté: 28 juil. 2022

DOI: https://doi.org/10.2478/cait-2022-0027

Mots clés
Dyna-Q-Learning, grid computing, T2SS, uncertainty, performance

© 2022 K. Bhargavi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Publié en ligne: 22 sept. 2022

Pages: 48 - 67

Reçu: 25 mars 2022

Accepté: 28 juil. 2022

DOI: https://doi.org/10.2478/cait-2022-0027

Mots clésDyna-Q-Learning, grid computing, T2SS, uncertainty, performance

© 2022 K. Bhargavi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Dyna-Q-Learning, grid computing, T2SS, uncertainty, performance