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Improving consolidation of virtual machine based on virtual switching overhead estimation

机译:基于虚拟交换开销估算的虚拟机整合

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In virtualized data centers, live virtual machine (VM) migration can increase energy efficiency by consolidating VMs on fewer servers. This problem is usually considered a Bin Packing Problem with server capacity constraints, such as CPU, memory and network bandwidth. In order to minimize the communication traffic within the data center network, existing research works used correlation-based strategy to consolidate VMs onto servers, which means that VMs with inter-traffic are consolidated as closely as possible, e.g. within a server or a rack. However, this strategy increases the traffic load of virtual switches on servers, and it causes a certain number of CPU cycles of servers to move traffic through virtual switches. A lack of consideration for the virtual switching overhead may increase the risk that VMs are not allocated enough resources, and consequently reduce VMs' performance. In this work, we conduct experiments to estimate the virtual switching overhead on server CPU resource, and based on the experiment results, we propose a virtual-switching-aware VM consolidation algorithm to address this problem. Experiments on representative data center workloads show that the overhead can occupy 10-30% of server's CPU resources. Additionally, our algorithm shows a much lower server capacity violation probability as compared with the baseline algorithm. (C) 2015 Elsevier Ltd. All rights reserved.
机译:在虚拟数据中心中,实时虚拟机(VM)迁移可通过在较少服务器上整合VM来提高能源效率。通常将此问题视为具有服务器容量限制(例如CPU,内存和网络带宽)的Bin Packing问题。为了最大程度地减少数据中心网络内的通信流量,现有研究工作使用基于相关性的策略将VM整合到服务器上,这意味着将流量相互关联的VM尽可能紧密地整合在一起,例如在服务器或机架中。但是,此策略增加了服务器上虚拟交换机的流量负载,并且导致一定数量的服务器CPU周期通过虚拟交换机转移流量。缺少对虚拟交换开销的考虑可能会增加VM未分配足够资源的风险,从而降低VM的性能。在这项工作中,我们进行实验以估计服务器CPU资源上的虚拟交换开销,并根据实验结果,提出一种可感知虚拟交换的VM整合算法来解决此问题。对代表性数据中心工作负载的实验表明,开销可以占用服务器CPU资源的10-30%。此外,与基准算法相比,我们的算法显示出更低的服务器容量侵害概率。 (C)2015 Elsevier Ltd.保留所有权利。

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