Title

A Study of Real-Time Scheduling Algorithms in Cluster Environment Based on Machine Learning

Authors

Ruiyang Zhang, Wenzhou-Kean University
Zehai Liu, Wenzhou-Kean University
Gengchen Tian, Wenzhou-Kean University
Yuhao Lu, Wenzhou-Kean University
Krishamoorthy Sujatha, Wenzhou-Kean University

Document Type

Conference Proceeding

Publication Date

1-1-2023

Abstract

Machine Learning is on the rise and is transforming industries across the board, from climate forecasting to stock price evaluation. In this study, we explore the use of machine learning in real-Time scheduling algorithms for cluster environments. Using the 'GWA-T-4 Auver Grid' dataset, we predict burst times of processes with an accuracy of over 87%. We then compare the performance of the FCFS and SJF scheduling algorithms using these predictions, and find that while SJF performs better, it is better suited for short processes, while FCFS is better for longer ones. Our results provide insight into the potential of machine learning in the realm of real-Time scheduling algorithms for cluster environments.

Publication Title

2023 3rd International Conference on Consumer Electronics and Computer Engineering, ICCECE 2023

First Page Number

682

Last Page Number

686

DOI

10.1109/ICCECE58074.2023.10135306

Recommended Citation

Zhang, Ruiyang; Liu, Zehai; Tian, Gengchen; Lu, Yuhao; and Sujatha, Krishamoorthy, "A Study of Real-Time Scheduling Algorithms in Cluster Environment Based on Machine Learning" (2023). Kean Publications. 344.
https://digitalcommons.kean.edu/keanpublications/344