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Introducing Queuing Network-Based Performance Awareness in Autonomic Systems

机译:在自主系统中引入基于排队网络的性能意识

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This paper advocates for the introduction of performance awareness in autonomic systems. The motivation is to be able to predict the performance of a target configuration when a self-* feature is planning a system reconfiguration.We propose a global and partially automated process based on queues and queuing networks models. This process includes decomposing a distributed application into black boxes, identifying the queue model for each black box and assembling these models into a queuing network according to the candidate target configuration. Finally, performance prediction is performed either through simulation or analysis.This paper sketches the global process and focuses on the black box model identification step. This step is automated thanks to a load testing platform enhanced with a workload control loop. Model identification is then based on statistical tests. The model identification process is illustrated by experimental results.
机译:本文主张在自主系统中引入性能意识。这样做的动机是能够在self- *功能计划系统重新配置时预测目标配置的性能。我们提出了一种基于队列和排队网络模型的全局且部分自动化的过程。此过程包括将分布式应用程序分解为黑匣子,为每个黑匣子标识队列模型,然后根据候选目标配置将这些模型组装到排队网络中。最后,通过仿真或分析来执行性能预测。本文概述了全局过程,并着重于黑匣子模型识别步骤。由于负载测试平台通过工作负载控制回路进行了增强,因此该步骤实现了自动化。然后基于统计检验进行模型识别。实验结果说明了模型的识别过程。

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