CloudScope: Diagnosing and Managing Performance Interference in Multi-tenant Clouds
CloudScope: Diagnosing and Managing Performance Interference in Multi-tenant Clouds
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DOI：
10.1109/mascots.2015.35
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发表时间：
2015-10
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通讯作者：
Xi Chen;Lukas Rupprecht;Rasha Osman;P. Pietzuch;F. Franciosi;W. Knottenbelt
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作者：
Xi Chen;Lukas Rupprecht;Rasha Osman;P. Pietzuch;F. Franciosi;W. Knottenbelt
虚拟机整合在云计算平台中具有吸引力，原因有几个，包括降低基础设施成本，降低能耗和易于管理。但是，虚拟化导致的共存工作负载之间的干扰可能会违反云平台保证的服务水平目标（SLO）。现有的解决方案，以尽量减少虚拟机（VM）之间的干扰，主要是基于全面的微基准或在线培训，这使得他们的计算密集型。在本文中，我们提出了CloudScope，一个系统，用于诊断干扰多租户云系统在一个轻量级的方式。CloudScope采用离散时间马尔可夫链模型来在线预测共存VM的性能干扰。它使用结果来优化（重新）分配虚拟机到物理机，并优化虚拟机管理程序配置，例如它可以使用的CPU共享，用于不同的工作负载。我们在Xen hypervisor之上实现了CloudScope，并使用一组CPU、磁盘和网络密集型工作负载以及一个真实的系统（MapReduce）进行了实验。我们的研究结果表明，CloudScope干扰预测的平均误差为9%。与默认调度程序相比，干扰感知调度程序可将VM性能提高多达10%。此外，虚拟机管理程序重新配置可以将网络吞吐量提高高达30%。
Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%.