使用LXC/KVM的单租户和多租户云实例的NAS基准内核

Anderson M. Maliszewski, Dalvan Griebler, C. Schepke, Alexander Ditter, D. Fey, L. G. Fernandes
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引用次数: 7

摘要

私有IaaS云对于科学工作负载和应用程序来说是一个很有吸引力的环境。它提供了一些优势,例如在单个节点和计算集群中几乎即时可用的高性能计算,方便研究人员和无法访问传统超级计算机的用户访问。此外,云基础设施提供了弹性和可扩展性,以确保和管理对系统的任何软件依赖,而不依赖于研究人员的第三方。然而,最大的挑战之一是在将这些应用程序从物理节点迁移到云环境时避免显著的性能下降。此外,我们对多租户云实例缺乏更多的研究调查。在本文中,我们的目标是在私有云条件下使用KVM和LXC虚拟化技术,对具有单租户和多租户云实例的科学应用程序进行性能比较评估。所有分析和评估都是基于NAS基准内核进行的,以模拟不同类型的工作负载。我们应用统计显著性检验来突出差异。结果表明,在93.75%的单租户实验中,运行在基于lxc的云实例上的应用程序优于基于kvm的云实例。关于多租户,LXC实例在45%的结果中优于KVM实例,其中性能差异并不像预期的那么显著。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The NAS Benchmark Kernels for Single and Multi-Tenant Cloud Instances with LXC/KVM
Private IaaS clouds are an attractive environment for scientific workloads and applications. It provides advantages such as almost instantaneous availability of high-performance computing in a single node as well as compute clusters, easy access for researchers, and users that do not have access to conventional supercomputers. Furthermore, a cloud infrastructure provides elasticity and scalability to ensure and manage any software dependency on the system with no third-party dependency for researchers. However, one of the biggest challenges is to avoid significant performance degradation when migrating these applications from physical nodes to a cloud environment. Also, we lack more research investigations for multi-tenant cloud instances. In this paper, our goal is to perform a comparative performance evaluation of scientific applications with single and multi-tenancy cloud instances using KVM and LXC virtualization technologies under private cloud conditions. All analyses and evaluations were carried out based on NAS Benchmark kernels to simulate different types of workloads. We applied statistic significance tests to highlight the differences. The results have shown that applications running on LXC-based cloud instances outperform KVM-based cloud instances in 93.75% of the experiments w.r.t single tenant. Regarding multi-tenant, LXC instances outperform KVM instances in 45% of the results, where the performance differences were not as significant as expected.
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