RMVS: Energy-Efficient VM Scheduling for Flexible Manufacturing in Sensor-Cloud Industrial IoT Environments

As sensor-cloud networks scale to support the growing demands of industrial Internet of Things (IIoT) applications, efficient virtual machine (VM) scheduling becomes essential for optimizing both energy efficiency and quality of service (QoS). In these environments, the collaboration between large-scale and lightweight models deployed across cloud resources introduces significant challenges in dynamic resource allocation, task scheduling, and energy-aware computing scenarios. A novel reinforcement learning-guided Monte Carlo VM scheduling (RMVS) method, which integrates Monte Carlo tree search (MCTS) and deep reinforcement learning (DRL) to enable intelligent and adaptive resource management, is proposed in this paper. The DRL model is used to provide prior knowledge, guiding the MCTS to efficiently explore candidate physical machines (PMs) and make better scheduling decisions. Notably, the integration of VM migration allows the system to dynamically adjust workloads, real-locate resources, and shut down idle servers, thus improving both the energy efficiency and CPU utilization. The proposed RMVS framework demonstrates significant improvements, reducing the makespan, energy consumption, and Service Level Agreement (SLA) violation rate by approximately 4.74%, 4.34%, and 11.67%, respectively, while improving CPU utilization by about 6.23%, outperforming state-of-the-art methods.