响应时间优化的分布式云资源分配

Matthias Keller, H. Karl
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引用次数: 21

摘要

在不久的将来,更多的计算资源将在不同的地理位置可用。为了最小化请求的响应时间,可以使用离用户更近的应用服务器来缩短网络往返时间。但是,如果使用的数据中心负载很高,这个优势就会被抵消,因为请求的处理时间也很重要。我们将请求响应时间建模为网络往返时间加上数据中心的处理时间。提出了一种可容设施选址问题的形式化方法,将处理时间建模为排队模型的停留时间。我们将讨论所使用的数据中心数量与由此产生的响应时间之间的\emph{帕累托权衡}关系。例如,使用更少的数据中心可以削减开支,但会导致高利用率、高响应时间和更少的收入。以前的工作提出了一个非线性成本函数。我们证明了它的\emph{凸性},并从两个方面利用了这一性质:首先,我们在控制最大逼近误差的同时,将凸模型转化为线性模型。其次,我们使用凸求解器而不是较慢的非线性求解器。网络拓扑的数值结果证明了我们的工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Response time-optimized distributed cloud resource allocation
In the near future many more compute resources will be available at different geographical locations. To minimize the response time of requests, application servers closer to the user can hence be used to shorten network round trip times. However, this advantage is neutralized if the used data centre is highly loaded as the processing time of requests is important as well. We model the request response time as the network round trip time plus the processing time at a data centre.We present a capacitated facility location problem formalization where the processing time is modelled as the sojourn time of a queueing model. We discuss the \emph{Pareto trade-off} between the number of used data centres and the resulting response time. For example, using fewer data centres could cut expenses but results in high utilization, high response time, and smaller revenues.Previous work presented a non-linear cost function. We prove its \emph{convexity} and exploit this property in two ways: First, we transform the convex model into a linear model while controlling the maximum approximation error. Second, we used a convex solver instead of a slower non-linear solver.Numerical results on network topologies exemplify our work.
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