Job resource prediction and completion status prediction on large-scale computing platforms

Jiale He; Tao Zhang; Yu Zheng

doi:10.1117/12.3026476

9 February 2024 Job resource prediction and completion status prediction on large-scale computing platforms

Jiale He, Tao Zhang, Yu Zheng

Proceedings Volume 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023); 1307309 (2024) https://doi.org/10.1117/12.3026476
Event: Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 2023, Changsha, China

Abstract

Predicting the resource consumption and completion status of jobs is beneficial to improve the scheduling performance of the system. Many studies have shown that job name can effectively improve the accuracy of prediction. Therefore, by mining the structural semantic information of job name, this paper introduces new features of job name habit, including job name length, number of job name elements, editing distance, and analyzes each substructure of job name, adding classification features after clustering. The introduced new features can better characterize the similarity between jobs and provide strong support for model prediction. Based on the model trained by the new feature data set, the prediction accuracy is significantly improved compared with the model that only introduces the job name.

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

Citation Download Citation

Jiale He, Tao Zhang, and Yu Zheng "Job resource prediction and completion status prediction on large-scale computing platforms", Proc. SPIE 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 1307309 (9 February 2024); https://doi.org/10.1117/12.3026476

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