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Predictive Control for Relative Performance Management

机译:相对绩效管理的预测控制

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摘要

This paper examines the implementation of Model Predictive Control (MPC) for relative performance management in a shared resources system. Finite Control Set MPC as a model-based control is integrated in a scheme of differentiated control for a multi classes virtualized software system. A dynamic model is estimated in block-oriented form with nonlinear compensation feedback system. The performance objective is to maintain response time on the reference levels or subject to the degree of priority between the user classes. Experiments are conducted in a two-classes of virtualized software system for several performance differentiation scenarios. The performance of Integral FCS-MPC is evaluated in comparison with Proportional Integral (PI) control. The results show that predictive control framework provides significant improvement in predictability and disturbance rejection procedure. Therefore, Integral FCS-MPC outperforms PI controller in terms of maintaining the stability of relative performance objectives.
机译:本文研究了模型预测控制(MPC)在共享资源系统中的相对性能管理的实现。有限控制集MPC作为基于模型的控件,集成在针对多类虚拟化软件系统的差异控制方案中。利用非线性补偿反馈系统,以面向块的形式估计一个动态模型。性能目标是将响应时间保持在参考级别上,或者以用户类之间的优先级为准。在两类虚拟化软件系统中针对几种性能差异方案进行了实验。与比例积分(PI)控件相比,对积分FCS-MPC的性能进行了评估。结果表明,预测控制框架在可预测性和干扰排除程序方面提供了显着改进。因此,就保持相对性能目标的稳定性而言,集成式FCS-MPC优于PI控制器。

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