Dynamic Derivation of Analytical Performance Models in Autonomic Computing Environments
Dynamic Derivation of Analytical Performance Models in Autonomic Computing Environments
复制标题
自主计算环境中分析性能模型的动态推导
DOI：
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发表时间：
2014
期刊：
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通讯作者：
D. Menasc
中科院分区：
文献类型：
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作者：
M. Awad;D. Menasc
导出分析性能模型需要对被建模的计算机系统的体系结构和行为有深入的了解。在自主计算环境中，考虑到这些环境的动态特性，这些详细的知识可能不容易获得（或者收集起来可能不切实际）。在本文中，我们提出了一个动态导出和参数化自主系统性能模型的框架。性能模型是通过观察实际系统的输入和输出参数（每个作业类的平均到达率和响应时间）之间的关系来推导和参数化的。本文展示了使用Apache OFBiz TM实现我们的方法的结果，并强调了推导模型的预测能力。
Deriving analytical performance models requires intimate knowledge of the architecture and behavior of the computer system being modeled. In autonomic computing environments, this detailed knowledge may not be readily available (or it may be impractical to gather) given the dynamic nature of these environments. In this paper, we present a framework for dynamically deriving and parameterizing performance models in autonomic systems. Performance models are derived and parameterized by observing the relationships between a real system’s input and output parameters (average arrival rates and response times for each job class). The paper shows the results of implementing our approach using Apache OFBiz TM and highlights the predictive power of the derived model.