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Efficient VM Memory Balancing Algorithm Design and Implementation

机译:高效的虚拟机内存平衡算法设计与实现

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A common strategy to manage memory resources of VMs under changing workloads is Dynamic VM memory management via hotplug. However, in order to estimate the VM working set size, most researchers are utilizing approaches that rely on kernel instrumentation but this most often results to high runtime overhead. This will result in system administrate to exercise a tradeoff between the estimate accuracy and system performance. The novelty of this work is to present a light weight accurate and transparent prediction algorithm for re-balancing memory resources among VMs. Experiments result attained on Dacapo and SPECjvm2008 from renowned benchmarks shows that with only 4% performance overhead our proposed method is capable of accurately adjusting virtual machine memory size on its real time requirements, and improve application performance in the virtual machine more than 10% better when virtual machine has 2 CPUs and 20% better when it has 4 CPUs. In case there is no free memory available our proposed method will try first to use host's free memory but if there is no free memory, it starts memory over-commitment for a fixed duration to help the requested VM complete its task.
机译:在不断变化的工作负载下管理虚拟机内存资源的常见策略是通过热插拔进行动态虚拟机内存管理。但是,为了估计VM工作集的大小,大多数研究人员正在利用依赖于内核检测的方法,但这通常会导致较高的运行时开销。这将导致系统管理员在估计精度和系统性能之间进行权衡。这项工作的新颖性在于提出一种轻量级的准确而透明的预测算法,用于在VM之间重新平衡内存资源。在Dacapo和SPECjvm2008上从著名基准测试中获得的实验结果表明,我们提出的方法仅以4%的性能开销就能根据其实时要求准确地调整虚拟机内存大小,并将虚拟机中的应用程序性能提高10%以上。虚拟机有2个CPU,而有4个CPU则性能提高20%。如果没有可用的可用内存,我们提出的方法将首先尝试使用主机的可用内存,但是如果没有可用的内存,它将在固定的时间内启动内存过量使用,以帮助请求的VM完成其任务。

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