An online resource efficient scheduling for colocation MLaaS cluster

Haodong Zou; Xin Chen; Jinghang Yu; Ling Wang; Kefeng Wu; Mingtao Ji

doi:10.1117/12.3026938

8 April 2024 An online resource efficient scheduling for colocation MLaaS cluster

Haodong Zou, Xin Chen, Jinghang Yu, Ling Wang, Kefeng Wu, Mingtao Ji

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130901W (2024) https://doi.org/10.1117/12.3026938
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

With the rapid growth of machine learning, Machine-Learning-as-a-Service (MLaaS) clusters appear to meet the needs of researchers. However, such clusters generally suffer from high resource utilization and energy consumption. For this reason, most work has focused on resource efficient scheduling for resource consolidation. However, in the process of resource consolidation, job performance is also affected, and it is a serious challenge to minimize energy consumption while ensuring quality of service (QoS) of jobs. In this paper, we analyze the differences in quality of service between offline and online jobs. For the performance interference problem caused by shared resource contention in online tasks, we portray the relationship between shared resources and job performance. As a result, we propose an online learning scheduling algorithm for colocation MLaaS cluster (OLSC), which based on alternating multiplier gradient descent, aims to guarantee the QoS of jobs and minimize cluster energy consumption. The results show that the cluster energy consumption under OLSC is only 59% of RI-FFD and 83% of RS-FFD under colocation MLaaS clusters.

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

Citation Download Citation

Haodong Zou, Xin Chen, Jinghang Yu, Ling Wang, Kefeng Wu, and Mingtao Ji "An online resource efficient scheduling for colocation MLaaS cluster", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130901W (8 April 2024); https://doi.org/10.1117/12.3026938

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