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Performance Prediction Based Workload Scheduling in Co-Located Cluster

Dongyang Ou, Yongjian Ren, Congfeng Jiang*

School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

* Corresponding Author: Congfeng Jiang. Email: email

(This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)

Computer Modeling in Engineering & Sciences 2024, 139(2), 2043-2067.


Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster, where the resources can be pooled in order to maximize data center resource utilization. Due to resource competition between batch jobs and online services, co-location frequently impairs the performance of online services. This study presents a quality of service (QoS) prediction-based scheduling model (QPSM) for co-located workloads. The performance prediction of QPSM consists of two parts: the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on random forest. On-line service QoS anomaly prediction is used to evaluate the influence of batch job mix on on-line service performance, and batch job completion time prediction is utilized to reduce the total waiting time of batch jobs. When the same number of batch jobs are scheduled in experiments using typical test sets such as CloudSuite, the scheduling time required by QPSM is reduced by about 6 h on average compared with the first-come, first-served strategy and by about 11 h compared with the random scheduling strategy. Compared with the non-co-located situation, QPSM can improve CPU resource utilization by 12.15% and memory resource utilization by 5.7% on average. Experiments show that the QPSM scheduling strategy proposed in this study can effectively guarantee the quality of online services and further improve cluster resource utilization.


Cite This Article

Ou, D., Ren, Y., Jiang, C. (2024). Performance Prediction Based Workload Scheduling in Co-Located Cluster. CMES-Computer Modeling in Engineering & Sciences, 139(2), 2043–2067.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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