Traditional data centers are shifted toward the cloud computing paradigm. These data centers support the increasing demand for computational and data storage that consumes a massive amount of energy at a huge cost to the cloud service provider and the environment. Considerable energy is wasted to constantly operate idle virtual machines (VMs) on hosts during periods of low load. Dynamic consolidation of VMs from overloaded or underloaded hosts is an effective strategy for improving energy consumption and resource utilization in cloud data centers. The dynamic consolidation of VM from an overloaded host directly influences the service level agreements (SLAs), utilization of resources, and quality of service (QoS) delivered by the system. We proposed an algorithm, namely, GradCent, based on the Stochastic Gradient Descent technique. This algorithm is used to develop an upper CPU utilization threshold for detecting overloaded hosts by using a real CPU workload. Moreover, we proposed a dynamic VM selection algorithm called Minimum Size Utilization (MSU) for selecting the VMs from an overloaded host for VM consolidation. GradCent and MSU maintain the trade-off between energy consumption minimization and QoS maximization under specified SLA goal. We used the CloudSim simulations with real-world workload traces from more than a thousand PlanetLab VMs. The proposed algorithms minimized energy consumption and SLA violation by 23% and 27.5% on average, compared with baseline schemes, respectively.

传统的数据中心已转向云计算模式。这些数据中心满足了对计算和数据存储的不断增长的需求，这些计算和数据存储消耗大量能源，而云服务提供商和环境的成本却很高。在低负载期间，要浪费大量的精力来持续运行主机上的空闲虚拟机（VM）。从过载或负载不足的主机动态整合VM是提高云数据中心能耗和资源利用率的有效策略。来自过载主机的VM的动态整合直接影响系统提供的服务级别协议（SLA），资源利用率和服务质量（QoS）。我们提出了一种算法，即GradCent，基于随机梯度下降技术。此算法用于通过使用实际CPU工作负载来开发CPU利用率上限阈值，以检测过载的主机。此外，我们提出了一种动态虚拟机选择算法，称为最小大小利用率（MSU），用于从过载主机中选择虚拟机以进行虚拟机合并。在指定的SLA目标下，GradCent和MSU保持能耗最小化和QoS最大化之间的权衡。我们将CloudSim模拟与来自一千多个PlanetLab VM的真实工作负载跟踪结合使用。与基线方案相比，所提出的算法分别将能耗和SLA违规最小化了23％和27.5％。