Research on distributed heterogeneous task scheduling and resource allocation algorithms based on deep learning

Qiu Zhen; Fan Xu; Wenpu Li; Fan Yang; Hongyu Wu; Huanhuan Li

doi:10.1117/12.3032073

6 June 2024 Research on distributed heterogeneous task scheduling and resource allocation algorithms based on deep learning

Qiu Zhen, Fan Xu, Wenpu Li, Fan Yang, Hongyu Wu, Huanhuan Li

Author Affiliations +

Proceedings Volume 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024); 1317518 (2024) https://doi.org/10.1117/12.3032073
Event: 4th International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 2024, Sanya, China

Abstract

With the rapid development and application of deep learning, its dataset size and network model are becoming increasingly large, and distributed model training is becoming increasingly popular. This article proposes a distributed heterogeneous task scheduling and resource allocation algorithm based on deep learning to address issues such as heterogeneity in resource usage, inability to predict task convergence time, communication time bottlenecks, and resource waste caused by static resource allocation during distributed collaborative training. This algorithm achieves dynamic scheduling and resource allocation of heterogeneous tasks and reduces task completion time in clusters. The experiment shows that the algorithm proposed in this article has significant improvements in both task completion time and system duration.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiu Zhen, Fan Xu, Wenpu Li, Fan Yang, Hongyu Wu, and Huanhuan Li "Research on distributed heterogeneous task scheduling and resource allocation algorithms based on deep learning", Proc. SPIE 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 1317518 (6 June 2024); https://doi.org/10.1117/12.3032073

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