Cloud data centers face various challenges, such as high energy consumption, environmental impact, and quality of service (QoS) requirements. Dynamic virtual machine (VM) consolidation is an effective approach to address these challenges, but it is a complex optimization problem that involves trade-offs between energy efficiency and QoS satisfaction. Moreover, the workload patterns in cloud data centers are often non-stationary and unpredictable, which makes it difficult to model them. In this paper, we propose a new method for dynamic VM consolidation that optimizes both energy efficiency and QoS objectives. Our approach is based on Markov chains and the artificial feeding birds (AFB) algorithm. Markov chains are used to model the resource utilization of each individual VM and PM based on the changes that happen in workload data. AFB algorithm is a metaheuristic optimization technique that mimics the behavior of birds in nature. We modify the AFB algorithm to suit the characteristics of the VM placement problem and to provide QoS-aware and energy-efficient solutions. Our approach also employs an online step detection method to capture variations in workload patterns. Furthermore, we introduce a new policy for VM selection from overloaded hosts, which considers the abrupt changes in the utilization processes of the VMs. The proposed algorithms are evaluated extensively using the CloudSim Toolkit with real workload data. The proposed system outperforms evaluation policies in multiple metrics, including energy consumption, SLA violations, and other essential metrics.

云数据中心面临着各种挑战，例如高能耗、环境影响和服务质量（QoS）要求。动态虚拟机 (VM) 整合是应对这些挑战的有效方法，但它是一个复杂的优化问题，涉及能源效率和 QoS 满意度之间的权衡。此外，云数据中心的工作负载模式通常是不稳定且不可预测的，这使得对其建模变得困难。在本文中，我们提出了一种动态虚拟机整合的新方法，可以优化能源效率和服务质量目标。我们的方法基于马尔可夫链和人工喂鸟（AFB）算法。马尔可夫链用于根据工作负载数据中发生的变化对每个单独的 VM 和 PM 的资源利用率进行建模。AFB 算法是一种模仿自然界鸟类行为的元启发式优化技术。我们修改 AFB 算法以适应虚拟机放置问题的特点，并提供 QoS 感知和节能的解决方案。我们的方法还采用在线步骤检测方法来捕获工作负载模式的变化。此外，我们引入了一种新的从过载主机中选择虚拟机的策略，该策略考虑了虚拟机使用过程的突然变化。使用 CloudSim 工具包和真实工作负载数据对所提出的算法进行了广泛的评估。所提出的系统在多个指标上都优于评估策略，包括能耗、SLA 违规情况和其他基本指标。