Measuring and characterizing the performance of interactive multi-tier cloud applications

Mohammad Y. Hajjat, N. ShankaranarayananP., A. Sivakumar, Sanjay G. Rao
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引用次数: 4

Abstract

In this paper, we conduct a detailed study characterizing the performance of multi-tier web applications on commercial cloud platforms and evaluate the potential of techniques to improve the resilience of such applications to performance fluctuations in the cloud. In contrast to prior works that have studied the performance of individual cloud services or that of compute-intensive scientific applications (e.g., map-reduce based), our study focuses on multi-tier web applications. Our work is conducted in the context of four real-world web applications which we instrumented to collect the overall response time and the time spent in each application tier, for each transaction. Our results indicate that cloud applications undergo frequent periods of poor performance that typically (i) are short-lived lasting a few minutes; and (ii) may be attributed to a small subset of application components, though different subsets may be involved at different times. While geo-distributing applications can help mitigate performance variability, coarse-grained approaches that merely choose the best performing data-center (DC) provide only modest benefits. More significant benefits could accrue, however, if combination of cloud services located across multiple datacenters (DCs) are chosen to serve each request.
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测量和描述交互式多层云应用程序的性能
在本文中,我们详细研究了商业云平台上多层web应用程序的性能特征,并评估了提高此类应用程序对云中性能波动的弹性的技术潜力。与之前研究单个云服务或计算密集型科学应用程序(例如,基于map-reduce)性能的工作相反,我们的研究侧重于多层web应用程序。我们的工作是在四个真实的web应用程序的上下文中进行的,我们对这些应用程序进行了检测,以收集每个事务在每个应用程序层中花费的总体响应时间和时间。我们的结果表明,云应用程序经常经历性能不佳的时期,通常是(i)短暂的,持续几分钟;并且(ii)可能归因于应用程序组件的一小部分,尽管不同的子集可能在不同的时间涉及。虽然地理分布应用程序可以帮助减轻性能变化,但仅选择性能最佳的数据中心(DC)的粗粒度方法只能提供有限的好处。但是,如果选择跨多个数据中心(dc)的云服务组合来处理每个请求,则可以获得更大的好处。
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