The widespread adoption of Industrial Internet of Things (IIoT)-based applications has driven the emergence and development of cloud-related computing paradigms with the ability to seamlessly leverage cloud resources. Heterogeneous resources, mobility factors in IoT, and dynamic behavior make it challenging for the corresponding virtual machine (VM) scheduling problem to address the processing effectiveness of application requests in these kinds of cloud environments. Based on reinforcement learning theory, this article proposes an online VM scheduling scheme (OSEC) for joint energy consumption and cost optimization that divides the scheduling process into two parts: VM allocation and VM migration. First, all the VMs and the physical machines (PMs) are regarded as a set of states and actions in the cloud environment, and the Q-learning feedback is used to achieve the iterative computation of Q-values to obtain the optimal parallel allocation sequence for multiple VMs. Then, VMs are migrated among the active PMs according to a grouping policy and the best-fit principle to achieve dynamic consolidation of the resources in the data center. Finally, experimental results show that compared with state-of-the-art algorithms under different conditions, the proposed method reduces energy consumption by approximately 18.25%, VM execution costs by approximately 21.34%, and service level agreement (SLA) violations by approximately 90.51%.

中文翻译：

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工业物联网网络中的实时虚拟机调度：一种强化学习方法


基于工业物联网 (IIoT) 的应用的广泛采用推动了云相关计算范式的出现和发展，并能够无缝利用云资源。物联网中的异构资源、移动性因素以及动态行为使得相应的虚拟机（VM）调度问题难以解决此类云环境中应用程序请求的处理有效性。基于强化学习理论，本文提出了一种联合能耗和成本优化的在线虚拟机调度方案（OSEC），将调度过程分为虚拟机分配和虚拟机迁移两部分。首先，将所有VM和物理机（PM）视为云环境中的一组状态和动作，利用Q学习反馈实现Q值的迭代计算，以获得最优并行分配序列对于多个虚拟机。然后，根据分组策略和最佳适应原则，在活跃的PM之间迁移虚拟机，实现数据中心资源的动态整合。最后，实验结果表明，与不同条件下的最先进算法相比，所提出的方法降低了约18.25％的能耗，约21.34％的VM执行成本，约90.51的服务级别协议（SLA）违规%。