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A VM-based Resource Management Method Using Statistics

机译:基于统计的基于虚拟机的资源管理方法

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摘要

Virtualization technology is one of the key technologies in cloud computing. Proper allocation of the virtual machines onto available hosts plays an important role on the performance optimization of cloud computing. At present, many resource management methods are based on the load model to predict the resource requirements of the application. However, a variety of heterogeneous hardware environments and virtualization technologies make it hard to efficiently allocate the resources based on existing methods. To address the problem, we proposed a virtual machine allocation strategy based on statistics. And The contribution of this paper include:(1) A load-resource model for estimating the resource demand of each virtual machine, (2) An algorithm for assigning virtual machines onto the hosts among resource pool according to the resources requirement of virtual machine. Experiments show that our load-resource model can accurately estimate the resource requirements of virtual machines for physical host, and our virtual machine assigning algorithm can achieve better load balancing compared with the first-fit and best-fit algorithm.
机译:虚拟化技术是云计算中的关键技术之一。将虚拟机正确分配到可用主机上对云计算的性能优化起着重要作用。当前,许多资源管理方法都基于负载模型来预测应用程序的资源需求。但是,各种各样的异构硬件环境和虚拟化技术使得难以基于现有方法有效地分配资源。为了解决这个问题,我们提出了一种基于统计的虚拟机分配策略。并且本文的贡献包括:(1)用于估计每个虚拟机的资源需求的负载-资源模型;(2)根据虚拟机的资源需求在资源池中的主机上分配虚拟机的算法。实验表明,我们的负载-资源模型可以准确地估计物理主机虚拟机的资源需求,并且与首选算法和最佳拟合算法相比,我们的虚拟机分配算法可以实现更好的负载平衡。

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