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Memory Management and Reuse Mechanism for Virtual Machine in Cloud Computing to Minimize Energy Consumption : A Review Paper

机译:云计算中虚拟机的内存管理和重用机制可最大程度地减少能耗:评论文章

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Cloud computing is an emerging computing technology for large data centers that maintains computational resources through the internet, rather than on local computers. VM migration provides the capability to balance the load, system maintenance and fault tolerance, etc. However, existing migration techniques, used to migrate virtual machines keeping memory images of VMs in host and skipping transfer of unchanged memory pages to reduce the amount of transfer data during migration, if number of migrations increases, number of memory images stored on host are also increased, this causes memory starvation. In this paper, a propose technique that reduces the size of memory image stored on source host before migration. When a VM migrates to other host, memory images of VM is kept in the source host after removing unwanted data according to the Probability factor. When the VM migrates back to the original host later, the kept memory image will be “reused”, i.e. data which are identical to the kept data will not be transferred and comparative to existing system the size of memory image is small. To validate this approach, evaluate the results using different threshold levels and probability factor of change in data. Proposed system required less memory to store the memory image and allow more VMs to be hosted. Specifically, proposed work is used to improve resource efficiency throughout by reducing the size of memory image that is stored on source host.
机译:云计算是用于大型数据中心的新兴计算技术,该技术通过Internet而不是在本地计算机上维护计算资源。 VM迁移提供了平衡负载,系统维护和容错等的功能。但是,现有的迁移技术用于迁移虚拟机,以将虚拟机的内存映像保留在主机中,并跳过未更改的内存页面的传输以减少传输数据量。在迁移期间,如果迁移数量增加,则主机上存储的内存映像数量也会增加,这会导致内存不足。在本文中,提出了一种在迁移之前减小存储在源主机上的内存映像大小的提议技术。当虚拟机迁移到其他主机时,根据概率因子删除不需要的数据后,虚拟机的内存映像将保留在源主机中。当VM稍后迁移回原始主机时,保留的内存映像将被“重用”,即与保留的数据相同的数据将不会被传输,并且与现有系统相比,内存映像的大小很小。为了验证这种方法,请使用不同的阈值水平和数据变化的概率因子来评估结果。拟议的系统需要较少的内存来存储内存映像并允许托管更多VM。特别是,通过减少存储在源主机上的内存映像的大小,建议的工作可用于整体上提高资源效率。

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