Probabilistic performance modeling of virtualized resource allocation
Probabilistic performance modeling of virtualized resource allocation
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DOI：
10.1145/1809049.1809067
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发表时间：
2010-06
期刊：
影响因子：
5.3
通讯作者：
B. J. Watson;M. Marwah;D. Gmach;Yuan Chen;M. Arlitt;Zhikui Wang
中科院分区：
文献类型：
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作者：
B. J. Watson;M. Marwah;D. Gmach;Yuan Chen;M. Arlitt;Zhikui Wang
虚拟化技术使组织能够根据工作负载波动和不断变化的业务需求动态调整其IT资源。然而，只有通过正式理解应用程序性能和虚拟化资源分配之间的关系，才能避免物理IT资源的过度配置或过载。在本文中，我们研究了虚拟化CPU分配，CPU争用和应用程序响应时间之间的概率关系，使自主控制器，以满足服务水平目标（SLO），同时更有效地利用IT资源。我们表明，只有最少的知识的应用程序和系统行为，我们的方法可以模拟的响应时间的概率分布，平均绝对误差小于6%，当与测量的响应时间分布。然后，我们证明了有用的概率方法与案例研究。我们将概率的基本定律应用到我们的模型中，以研究CPU分配和争用是否以及如何影响应用程序响应时间，并纠正它们对CPU利用率的影响。我们发现，在某些分配状态下，建模的响应时间分布之间的平均绝对差异为8-10%，当我们添加CPU争用时，也有类似的差异。这种方法是通用的，也应该适用于非CPU虚拟化资源和其他性能建模问题。
Virtualization technologies enable organizations to dynamically flex their IT resources based on workload fluctuations and changing business needs. However, only through a formal understanding of the relationship between application performance and virtualized resource allocation can over-provisioning or over-loading of physical IT resources be avoided. In this paper, we examine the probabilistic relationships between virtualized CPU allocation, CPU contention, and application response time, to enable autonomic controllers to satisfy service level objectives (SLOs) while more effectively utilizing IT resources. We show that with only minimal knowledge of application and system behaviors, our methodology can model the probability distribution of response time with a mean absolute error of less than 6% when compared with the measured response time distribution. We then demonstrate the usefulness of a probabilistic approach with case studies. We apply basic laws of probability to our model to investigate whether and how CPU allocation and contention affect application response time, correcting for their effects on CPU utilization. We find mean absolute differences of 8-10% between the modeled response time distributions of certain allocation states, and a similar difference when we add CPU contention. This methodology is general, and should also be applicable to non-CPU virtualized resources and other performance modeling problems.