Dongyang Ou, Yongjian Ren, Congfeng Jiang*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2043-2067, 2024, DOI:10.32604/cmes.2023.029987
Abstract 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… More >
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