ODRL: Application of Reinforcement Learning in Priority Scheduling for Running Cost Optimization

Due to the heterogeneity of workloads and the randomness and complexity of hybrid scheduling environment, How to address the challenge of minimizing data center running costs while ensuring workloads SLA has emerged as a significant research problem. To address this, we propose an Optimal Deep Reinforcement Learning Model for Running Cost Optimization in Hybrid Environments ( ODRL ). Firstly, we propose Running Cost Model ( RCM ) that analyzes data center operating costs from two perspectives based on different running patterns of the workload and node affinity constraints: minimizing computing node utilization and resource operating costs. Subsequently, we propose a Priority-Aware Scheduling Algorithm based on Deep Reinforcement Learning( PASD ) that use AHP-TOPSIS to quantify workload characterization, get real-time feedback for environment and update historical experience through prioritized experience replay. Finally, extensive experimental results validate the effectiveness of the proposed model. In comparison to Kube-Scheduler , ODRL achieves a significant 15% improvement in reducing data center running cost. Additionally, there is a 7.8% increase in CPU utilization and a 16.7% increase in memory utilization.