In the age of the information explosion, the energy demand for cloud data centers has increased markedly; hence, reducing the energy consumption of cloud data centers is essential. Dynamic virtual machine VM consolidation, as one of the effective methods for reducing energy energy consumption is extensively employed in large cloud data centers. It achieves the energy reductions by concentrating the workload of active hosts and switching idle hosts into low-power state; moreover, it improves the resource utilization of cloud data centers. However, the quality of service (QoS) guarantee is fundamental for maintaining dependable services between cloud providers and their customers in the cloud environment. Therefore, reducing the power costs while preserving the QoS guarantee are considered as the two main goals of this study. To efficiently address this problem, the proposed VM consolidation approach considers the current and future utilization of resources through the host overload detection (UP-POD) and host underload detection (UP-PUD). The future utilization of resources is accurately predicted using a Gray-Markov-based model. In the experiment, the proposed approach is applied for real-world workload traces in CloudSim and were compared with the existing benchmark algorithms. Simulation results show that the proposed approaches significantly reduce the number of VM migrations and energy consumption while maintaining the QoS guarantee.

在信息爆炸时代，对云数据中心的能源需求已显着增加。因此，减少云数据中心的能耗至关重要。大型云数据中心广泛采用动态虚拟机VM整合作为降低能耗的有效方法之一。通过集中活动主机的工作量并将空闲主机切换为低功耗状态，可以实现节能。此外，它还提高了云数据中心的资源利用率。但是，服务质量（QoS）保证对于在云环境中维护云提供商及其客户之间的可靠服务至关重要。因此，在保持QoS保证的同时降低功耗是本研究的两个主要目标。为了有效解决此问题，建议的VM合并方法通过主机过载检测（UP-POD）和主机欠载检测（UP-PUD）考虑当前和将来的资源利用。使用基于灰色马尔可夫的模型可以准确地预测资源的未来利用。在实验中，所提出的方法适用于CloudSim中的实际工作负载跟踪，并与现有的基准算法进行了比较。仿真结果表明，该方法在保持QoS保证的同时，显着减少了VM迁移的次数和能耗。使用基于灰色马尔可夫的模型可以准确地预测资源的未来利用。在实验中，所提出的方法适用于CloudSim中的实际工作负载跟踪，并与现有的基准算法进行了比较。仿真结果表明，该方法在保持QoS保证的同时，显着减少了VM迁移的数量和能耗。使用基于灰色马尔可夫的模型可以准确地预测资源的未来利用。在实验中，所提出的方法适用于CloudSim中的实际工作负载跟踪，并与现有的基准算法进行了比较。仿真结果表明，该方法在保持QoS保证的同时，显着减少了VM迁移的数量和能耗。