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Energy-efficient framework for virtual machine consolidation in cloud data centers

机译:云数据中心虚拟机整合的节能框架

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With the advent of the era of cloud computing, the high energy consumption of cloud computing data centers has become a prominent problem, and how to reduce the energy consumption of cloud computing data center and improve the efficiency of data center has become the research focus of researchers all the world. In a cloud environment, virtual machine consolidation (VMC) is an effective strategy that can improve the energy efficiency. However, at the same time, in the process of virtual machine consolidation, we need to deal with the tradeoff between energy consumption and excellent service performance to meet service level agreement (SLA). In this paper, we propose a new virtual machine consolidation framework for achieving better energy efficiency-Improved Underloaded Decision (IUD) algorithm and Minimum Average Utilization Difference (MAUD) algorithm. Finally, based on real workload data on PlanetLab, experiments have been done with the cloud simulation platform CloudSim. The experimental result shows that the proposed algorithm can reduce the energy consumption and SLA violation of data centers compared with existing algorithms, improving the energy efficiency of data centers.
机译:随着云计算时代的到来,云计算数据中心的高能耗已成为一个突出的问题,如何降低云计算数据中心的能耗并提高数据中心的效率已成为云计算的研究重点。世界各地的研究人员。在云环境中,虚拟机整合(VMC)是可以提高能源效率的有效策略。但是,与此同时,在虚拟机整合过程中,我们需要处理能耗与出色的服务性能之间的折衷,以达到服务水平协议(SLA)。在本文中,我们提出了一个新的虚拟机整合框架以实现更高的能源效率,即改进的欠载决策(IUD)算法和最小平均利用率差异(MAUD)算法。最后,根据PlanetLab上的实际工作量数据,已经使用云仿真平台CloudSim进行了实验。实验结果表明,与现有算法相比,该算法可以减少数据中心的能耗和SLA违规,提高了数据中心的能源效率。

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