With the rapid increase of memory consumption by applications running on cloud data centers, we need more efficient memory management in a virtualized environment. Exploiting huge pages becomes more critical for a virtual machine’s performance when it runs large working set size programs. Programs with large working set sizes are more sensitive to memory allocation, which requires us to quickly adjust the virtual machine’s memory to accommodate memory phase changes. It would be much more efficient if we could adjust virtual machines’ memory at the granularity of huge pages. However, existing virtual machine memory reallocation techniques, such as ballooning, do not support huge pages. In addition, in order to drive effective memory reallocation, we need to predict the actual memory demand of a virtual machine. We find that traditional memory demand estimation methods designed for regular pages cannot be simply ported to a system adopting huge pages. How to adjust the memory of virtual machines timely and effectively according to the periodic change of memory demand is another challenge we face. This paper proposes a dynamic huge page based memory balancing system (HPMBS) for efficient memory management in a virtualized environment. We first rebuild the ballooning mechanism in order to dispatch memory in the granularity of huge pages. We then design and implement a huge page working set size estimation mechanism which can accurately estimate a virtual machine’s memory demand in huge pages environments. Combining these two mechanisms, we finally use an algorithm based on dynamic programming to achieve dynamic memory balancing. Experiments show that our system saves memory and improves overall system performance with low overhead.

中文翻译：

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大页面友好虚拟化内存管理

随着运行在云数据中心上的应用程序内存消耗的快速增加，我们需要在虚拟化环境中进行更高效的内存管理。当虚拟机运行大型工作集大小的程序时，利用大页面对虚拟机的性能变得更加重要。大工作集大小的程序对内存分配更敏感，这就需要我们快速调整虚拟机的内存以适应内存阶段的变化。如果我们可以在大页面的粒度上调整虚拟机的内存，效率会高得多。但是，现有的虚拟机内存重新分配技术（例如膨胀）不支持大页面。此外，为了驱动有效的内存重新分配，我们需要预测虚拟机的实际内存需求。我们发现为常规页面设计的传统内存需求估计方法不能简单地移植到采用大页面的系统中。如何根据内存需求的周期性变化及时有效地调整虚拟机的内存是我们面临的另一个挑战。本文提出了一种基于动态大页面的内存平衡系统（HPMBS），用于在虚拟化环境中进行高效的内存管理。我们首先重建膨胀机制，以便以大页面的粒度调度内存。然后我们设计并实现了一个大页面工作集大小估计机制，可以准确估计虚拟机在大页面环境中的内存需求。结合这两种机制，我们最终使用基于动态规划的算法来实现动态内存平衡。