On the Predictive Properties of Performance Models Derived through Input-Output Relationships
On the Predictive Properties of Performance Models Derived through Input-Output Relationships
复制标题
论通过投入产出关系推导出的绩效模型的预测特性
DOI：
10.1007/978-3-319-10885-8_7
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
D. Menascé
中科院分区：
文献类型：
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作者：
M. Awad;D. Menascé
当对被建模系统的功能和行为知之甚少和/或难以通过测量获得模型参数时，构建分析性能模型是一项挑战。本文提出了一种方法，通过观察一个真实的系统的输入输出关系，推导出分析模型参数，解决了这个问题。更具体地，输入（即，每个作业类的到达率）和输出（即，每个作业类的平均响应时间）测量用于估计系统的GPRS网络模型的每类服务需求和服务器数量。该模型称为计算模型（CM），为用于导出CM的相同输入值提供相同的输出值。重要的问题是CM是否具有预测能力，即，CM能否预测在不同输入值的真实的系统中观察到的输出值？CM的参数通过求解非线性优化问题来获得。本文通过实验表明，CM是相对稳健的，并具有预测能力的范围内的输入值。
Building an analytical performance model is a challenge when little is known about the functionality and behavior of the system being modeled and/or when obtaining model parameters through measurements is difficult. This paper addresses this problem by presenting an approach that derives analytic model parameters by observing the input-output relationships of a real system. More specifically, input (i.e., arrival rates for each job class) and output (i.e., average response time for each job class) measurements are used to estimate the per-class service demands and number of servers for a Queuing Network model of the system. This model, called the computed model (CM), provides the same output values for the same input values used to derive the CM. The important question is whether the CM has predictive power, i.e., can the CM predict the output values that would be observed in the real system for different values of the input? The CM’s parameters are obtained by solving a non-linear optimization problem. The paper shows through experiments that the CM is relatively robust and has predictive power over a range of input values.