Deep Learning-Based Dynamic Scheduling with Multi-Agents Supporting Scalability in Edge Computing Environments


KIPS Transactions on Software and Data Engineering, Vol. 12, No. 9, pp. 399-406, Sep. 2023
https://doi.org/10.3745/KTSDE.2023.12.9.399,   PDF Download:
Keywords: Dynamic Scheduling, Deep Learning, Cloud computing, Edge Cloud, Multi-agent
Abstract

Cloud computing has been evolved to support edge computing architecture that combines fog management layer with edge servers. The main reason why it is received much attention is low communication latency for real-time IoT applications. At the same time, various cloud task scheduling techniques based on artificial intelligence have been proposed. Artificial intelligence-based cloud task scheduling techniques show better performance in comparison to existing methods, but it has relatively high scheduling time. In this paper, we propose a deep learning-based dynamic scheduling with multi-agents supporting scalability in edge computing environments. The proposed method shows low scheduling time than previous artificial intelligence-based scheduling techniques. To show the effectiveness of the proposed method, we compare the performance between previous and proposed methods in a scalable experimental environment. The results show that our method supports real-time IoT applications with low scheduling time, and shows better performance in terms of the number of completed cloud tasks in a scalable experimental environment.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
J. Lim, "Deep Learning-Based Dynamic Scheduling with Multi-Agents Supporting Scalability in Edge Computing Environments," KIPS Transactions on Software and Data Engineering, vol. 12, no. 9, pp. 399-406, 2023. DOI: https://doi.org/10.3745/KTSDE.2023.12.9.399.

[ACM Style]
JongBeom Lim. 2023. Deep Learning-Based Dynamic Scheduling with Multi-Agents Supporting Scalability in Edge Computing Environments. KIPS Transactions on Software and Data Engineering, 12, 9, (2023), 399-406. DOI: https://doi.org/10.3745/KTSDE.2023.12.9.399.