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Network aware load-balancing via parallel VM migration for data centers

机译:通过数据中心的并行VM迁移实现网络感知的负载平衡

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It becomes a challenge to design an efficient load balancing method via live virtual machine (VM) migration without degrading application performance. Two major performance impacts on hosted applications that run on a VM are the system load balancing degree and the total time till a balanced state is reached. Existing load balancing methods usually ignore the VM migration time overhead. In contrast to sequential migration-based load balancing, this paper proposes using a network-topology aware parallel migration to speed up the load balancing process in a data center. We transform the VM migration-based multi-resource load-balancing problem into a minimum weighted matching problem over a weighted bipartite graph. By obtaining the minimum weighted matching pairs through the Hungarian method, we parallel migrate multiple VMs from overloaded hosts to underutilized hosts to reduce the time it takes to reach a load balanced state. The experimental results show that our algorithm not only obtains a compatible multi-resource load balancing performance but also improves the balanced time which results in at most a 10% throughput gain by assuming a large batch application running on all VMs.
机译:在不降低应用程序性能的情况下,通过实时虚拟机(VM)迁移设计一种有效的负载平衡方法成为一项挑战。对在VM上运行的托管应用程序而言,两个主要的性能影响是系统负载平衡度和达到平衡状态所需的总时间。现有的负载平衡方法通常会忽略VM迁移时间的开销。与基于顺序迁移的负载平衡相反,本文提出使用可感知网络拓扑的并行迁移来加快数据中心的负载平衡过程。我们将基于虚拟机迁移的多资源负载平衡问题转换为加权二部图上的最小加权匹配问题。通过使用匈牙利方法获得最小的加权匹配对,我们将多个VM从过载的主机并行迁移到未充分利用的主机,以减少达到负载平衡状态所需的时间。实验结果表明,我们的算法不仅获得了兼容的多资源负载平衡性能,而且还改善了平衡时间,假设在所有VM上运行大批处理应用程序,则最多可将吞吐量提高10%。

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