在多核虚拟化环境中理解、建模和改进web应用程序的性能

Xi Chen, C. Ho, Rasha Osman, P. Harrison, W. Knottenbelt
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引用次数: 19

摘要

随着计算产业进入云时代,多核架构和虚拟化技术正在取代传统的IT基础设施。然而,在多核虚拟化环境中,应用程序和系统资源之间的复杂关系还没有得到很好的理解。web服务和在线金融应用程序等工作负载需要高性能,但基准分析表明,这些应用程序并不能从更高数量的核心中获得最佳收益。在本文中,我们试图理解运行在多核架构上的网络/CPU密集型应用程序的可伸缩性行为。我们首先对Petstore web应用程序进行基准测试,注意到与每个核心工作负载相关的系统不平衡。在确定了这种现象的原因之后,我们提出了一个队列模型,当适当地参数化时,它反映了我们的基准测试结果中最多8个核心的趋势。我们方法的关键是提供一个细粒度的模型,该模型结合了操作系统和多个CPU内核的特性。对模型的分析提出了一种直接的方法来缓解所观察到的瓶颈,这可以通过在我们的VM中部署多个虚拟网卡来实际实现。接下来,我们进行盲预测,以预测多个虚拟网卡的性能。验证结果表明,该模型能够在8% ~ 26%的相对误差范围内预测出预期性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding, modelling, and improving the performance of web applications in multicore virtualised environments
As the computing industry enters the Cloud era, multicore architectures and virtualisation technologies are replacing traditional IT infrastructures. However, the complex relationship between applications and system resources in multicore virtualised environments is not well understood. Workloads such as web services and on-line financial applications have the requirement of high performance but benchmark analysis suggests that these applications do not optimally benefit from a higher number of cores. In this paper, we try to understand the scalability behaviour of network/CPU intensive applications running on multicore architectures. We begin by benchmarking the Petstore web application, noting the systematic imbalance that arises with respect to per-core workload. Having identified the reason for this phenomenon, we propose a queueing model which, when appropriately parametrised, reflects the trend in our benchmark results for up to 8 cores. Key to our approach is providing a fine-grained model which incorporates the idiosyncrasies of the operating system and the multiple CPU cores. Analysis of the model suggests a straightforward way to mitigate the observed bottleneck, which can be practically realised by the deployment of multiple virtual NICs within our VM. Next we make blind predictions to forecast performance with multiple virtual NICs. The validation results show that the model is able to predict the expected performance with relative errors ranging between 8 and 26 per cent.
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