Revitalizing the single batch environment: a ‘Quest’ to achieve fairness and efficiency

Abstract

In the realm of computer systems, efficient utilization of the CPU (Central Processing Unit) has always been a paramount concern. Researchers and engineers have long sought ways to optimize process execution on the CPU, leading to the emergence of CPU scheduling as a field of study. In this research, we have analyzed the single offline batch processing and investigated other sophisticated paradigms such as time-sharing operating systems and wildly used algorithms, and their shortcomings. Our work is directed toward two fundamental aspects of scheduling: efficiency and fairness. We propose a novel algorithm for batch processing that operates on a preemptive model, dynamically assigning priorities based on a robust ratio, employing a dynamic time slice, and utilizing periodic sorting to achieve fairness. By engineering this responsive and fair model, the proposed algorithm strikes a delicate balance between efficiency and fairness, providing an optimized solution for batch scheduling while ensuring system responsiveness.

Keywords:

• CPU scheduling
• batch processing
• efficiency
• fairness
• greedy algorithm
• optimization

Acknowledgments

The authors express their gratitude to the reviewers, editor, and journal administrator for their cooperation. Special thanks to John Schroder for his unwavering assistance, thoughtful insights, and moral support throughout the project.

Data Availability

The data that support the findings of this study are openly available at GitHub

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://elixir.bootlin.com/linux/v5.19.9/source/kernel/sched/fair.c#L7429

2 following a similar pythonic adaptation used:https://github.com/SanchithHegde/completely-fair-scheduler

3 https://man7.org/linux/man-pages/man7/sched.7.html

4 https://numpy.org/doc/1.16/reference/routines.random.html

5 https://elixir.bootlin.com/linux/v5.19.9/source/kernel/sched/fair.c##L7429

Additional information

Notes on contributors

Supriya Manna

Supriya Manna is a final-year student in the Department of Computer Science & Engineering at SRM University AP. He is interested in the design and analysis of algorithms, machine learning, and the societal aspects of machine learning.

Krishna Siva Prasad Mudigonda

Krishna Siva Prasad Mudigonda is presently an Assistant Professor in SRM University AP, Andhra Pradesh, India. He completed PhD from Computer Science and Engineering department, Visvesvaraya National Institute Technology, Nagpur, India. He received his M.Tech and B.Tech degrees from Jawaharlal Nehru Technological university, Kakinada, Andhra Pradesh, India. His research area includes natural language processing, deep learning and text processing. He published articles in various reputed journals.