在云中放置应用程序级延迟需求

Fangzhe Chang, R. Viswanathan, Thomas L. Wood
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引用次数: 20

摘要

CPU和设备虚拟化技术允许应用程序托管在云平台上;由此产生的一些好处是更低的成本和更大的弹性。在这样的云托管应用程序中,一些组件驻留在云上,而其他组件(如终端用户和与物理设备绑定的组件)位于云之外。许多应用程序,例如电信服务,就必须在多少时间内完成某些过程而言,具有严格的延迟要求。应用程序延迟很大程度上取决于云内和云外所有交互组件的位置。在本文中,我们研究了在云中确定应用程序组件的最佳位置以满足应用程序的延迟要求的问题。我们提出了放置问题的精确公式,其中包括云平台的规范和应用程序级延迟需求的集合延迟表达式。我们展示了消息序列图(MSCs),一种广泛使用的描述应用程序执行的机制,可以自然地转化为我们的集体延迟表达式的形式。我们提出了利用网络拓扑的欧几里得三角不等式性质的布局算法:(a)确定最优布局的精确算法,但它具有最坏情况指数运行时间,以及(b)确定接近最优布局的算法,具有快速多项式运行时间。此外,我们提出了一种精确的技术,将放置问题划分为更小的子问题,从而实现更高的效率和准确性。我们在一个代表性的电信应用——LTE移动管理实体(MME)的分布式部署——上评估了算法的性能。我们的评估结果表明,我们的近似算法在寻找成功的放置位置方面比随机放置高出49%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Placement in Clouds for Application-Level Latency Requirements
CPU and device virtualization technology allows applications to be hosted on cloud platforms; some of the resulting benefits are lower cost and greater elasticity. In such cloud hosted applications, some components reside on the cloud while others, such as end users and components tied to physical devices, are located outside the cloud. Many applications, e.g., telecom services, have stringent latency requirements in terms of within how much time certain procedures must be completed. The application latency is strongly determined by the locations of all the interacting components that are both within and outside the cloud. In this paper, we study the problem of determining the optimal placement of the application components in the cloud so that the latency requirements of the application can be met. We present a precise formulation of the placement problem which includes a specification of the cloud platform, and collective latency expressions for application-level latency requirements. We show that Message Sequence Charts (MSCs), a widely-used mechanism for describing the execution of application procedures, can be naturally translated into our formalism of collective latency expressions. We present placement algorithms that exploit the Euclidean triangular inequality property of network topologies: (a) an exact algorithm for determining the most optimal placement but which has a worst-case exponential running time, and (b) an algorithm for determining a close to-optimal placement that has a fast polynomial running time. Additionally, we present an exact technique for partitioning a placement problem into smaller sub problems so that greater efficiency and accuracy can be achieved. We evaluate the performance of the algorithms on a representative telecom application --- a distributed deployment of the LTE Mobility Management Entity (MME). Our evaluation results show that our approximate algorithm can outperform a random placement by up to 49% for finding a successful placement.
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