Open AccessA large-scale task scheduling algorithm based on clustering and duplication
Author(s) -
Wengang Huang,
Zhichen Shi,
Xunhua Zheng,
Cen Chen,
Kenli Li
Publication year - 2022
Publication title -
journal of smart environments and green computing
Language(s) - English
Resource type - Journals
ISSN - 2767-6595
DOI - 10.20517/jsegc.2021.13
Subject(s) - computer science , scheduling (production processes) , fair share scheduling , two level scheduling , dynamic priority scheduling , rate monotonic scheduling , parallel computing , gang scheduling , round robin scheduling , fixed priority pre emptive scheduling , distributed computing , schedule , cluster analysis , algorithm , mathematical optimization , artificial intelligence , mathematics , operating system
Aim: Our research aims to explore a fast and efficient scheduling algorithm. The purpose is to schedule large-scale tasks on a limited number of processors reasonably while improving resource utilization. Methods: This paper proposes a clustering and duplication-based method for large-scale task scheduling on a limited amount of processors. We cluster large-scale task to reduce the scale of the task in our method at first. Second, duplication-based task scheduling is carried out. Third, we optimize the local effect more precisely by deduplication in the last stage. Results: We compare our algorithm with the state-of-the-art algorithms in the article. The results demonstrate that our scheduling scheme obtains about 30% optimization compared to existing large-scale scheduling methods and runs roughly ten times faster than existing duplication-based algorithms when scheduling large-scale tasks to a limited number of processors as compared to similar algorithms. Conclusion: In this paper, we propose a task scheduling algorithm that can decrease the scheduling time of large- scale tasks on a limited number of processors and speed up the global execution time of the task. Further, we will study large-scale task scheduling on heterogeneous processor clusters. Keywords