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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Virtual Machine Consolidation with Minimization of Migration Thrashing for Cloud Data Centers
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Virtual Machine Consolidation with Minimization of Migration Thrashing for Cloud Data Centers

机译:虚拟机整合,最小化云数据中心延迟迁移

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Cloud data centers consume huge amount of electrical energy bringing about in high operating costs and carbon dioxide emissions. Virtual machine (VM) consolidation utilizes live migration of virtual machines (VMs) to transfer a VM among physical servers in order to improve the utilization of resources and energy efficiency in cloud data centers. Most of the current VM consolidation approaches tend to aggressive-migrate for some types of applications such as large capacity application such as speech recognition, image processing, and decision support systems. These approaches generate a high migration thrashing because VMs are consolidated to servers according to VM’s instant resource usage without considering their overall and long-term utilization. The proposed approach, dynamic consolidation with minimization of migration thrashing (DCMMT) which prioritizes VM with high capacity, significantly reduces migration thrashing and the number of migrations to ensure service-level agreement (SLA) since it keeps VMs likely to suffer from migration thrashing in the same physical servers instead of migrating. We have performed experiments using real workload traces compared to existing aggressive-migration-based solutions; through simulations, we show that our approach improves migration thrashing metric by about 28%, number of migrations metric by about 21%, and SLAV metric by about 19%.
机译:云数据中心消耗大量的电能,从而实现高运营成本和二氧化碳排放。虚拟机(VM)合并利用虚拟机(VM)的实时迁移来在物理服务器之间传输VM,以提高云数据中心的资源和能效的利用率。大多数当前的VM整合方法倾向于积极迁移某些类型的应用,例如大容量应用,例如语音识别,图像处理和决策支持系统。这些方法生成高迁移次振荡,因为VM的即时资源使用情况将VMS合并到服务器,而不考虑其总体和长期利用率。提出的方法,动态整合,最小化迁移延迟(DCMMT),该删除(DCMMT)优先考虑高容量,显着降低了迁移次数和迁移的数量,以确保服务级别协议(SLA),因为它使VMS可能会遭受迁移的迁移延迟相同的物理服务器而不是迁移。与现有的基于侵略性迁移的解决方案相比,我们使用真实工作量迹线进行了实验;通过仿真,我们表明我们的方法将迁移缩小度量提高了大约28%,迁移次数约为21%,并且SLAV度量约为19%。

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