PPO-RR: virtual machine scheduling algorithm and deep reinforcement learning method for green computing

Qi, Yunrui

doi:10.1177/18724981251397520

PPO-RR: virtual machine scheduling algorithm and deep reinforcement learning method for green computing

2026-01-01

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DOI：10.1177/18724981251397520

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The ever-growing number of energy requirements needed in cloud data centers has led to the rising use of intelligent, sustainable and environmental scheduling algorithms. The study proposes a new hybrid algorithm that is a fusion of Proximal Policy Optimization (PPO) and Round Robin (RR), and is called PPO-RR, to enhance the efficiency of virtual machine (VM) scheduling and reduce the amount of energy consumption in a virtualized cloud system. The suggested approach follows the strategy of reinforcement learning: using the feedback about the system state, it can actively optimize the scheduling decisions to improve resource utilization and eliminate unnecessary waste of power in idle conditions.Experiments were carried using an artificially created cloud environment dataset. The PPO-RR model was seen to have shown significant performance gains over the conventional RR and heuristic scheduling techniques. To be more precise, PPO-RR achieved the accuracy of 95.3%, precision of 94.6%, recall of 95.4, and F1-score of 94.9. Such findings demonstrate the superiority of the model in its capacity to assign the VMs to the hosts appropriately during different workloads.With regard to energy measures, PPO-RR saved 1520 kWh to 1235 kWh translating to about 56.3 percent of energy saved as compared to conventions practices. The PPO-RR confusion matrix shows that the True Positives (TP) are 420, True Negatives (TN) are 360, False Positives (FP) are 15 and False Negatives (FN) are 25 which means that the accuracy of the classification of the decisions in scheduling is high.The model offers dynamic scheduling with performance and energy benefits compared to the traditional schedulers because the PPO-RR model adapts to the work load profile and system conditions in a real-time fashion. Despite the fact that this study shows PPO-RR to positively perform in various metrics, no formal statistical test of significance (e.g., t-test) was conducted and will be done in subsequent research.In sum, the PPO-RR approach provides a flexible and extensible mechanism to implement sustainable cloud computing, since it contributes to satisfying the objectives of green computing with the help of well-organized schedules.

Keywords:

proximal policy optimization

virtual machine scheduling

green computing

round robin

energy efficiency

deep reinforcement learning

PPO-RR: virtual machine scheduling algorithm and deep reinforcement learning method for green computing

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