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Managing performance and resources in software systems using nonlinear predictive control

机译:使用非线性预测控制管理软件系统中的性能和资源

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Management of quality of service performance and resources in a shared resource environment is vital to many business domains to achieve business objectives. These management systems provide agreed levels of quality of service to their clients while allocating limited available resources among them. It is well known that the behavior of such software systems illustrate nonlinear characteristics, imposing difficulties to model and control the system. This paper proposes a nonlinear model predictive control technique for managing the performance and resources in such a shared resource environment. In particular, a block-oriented Wiener model is utilized to represent the software system as a multi-input and multi-output model in series with static nonlinear components at the outputs. Then a predictive control system is designed by compensating the estimated nonlinearities with their inverse. The simulation results show that the proposed nonlinear model predictive control mechanism has significantly improved the performance and resource management at runtime over the linear predictive control counterpart.
机译:管理在共享资源环境中的服务性能和资源质量管理对许多商业领域至关重要,以实现业务目标。这些管理系统为客户提供商定的服务质量水平,同时分配有限的可用资源。众所周知,这种软件系统的行为说明了非线性特性,对模型和控制系统的困难施加困难。本文提出了一种非线性模型预测控制技术,用于管理如此共享资源环境中的性能和资源。具体地,面向块的维纳模型用于将软件系统作为串联的多输入和多输出模型与输出的静态非线性分量串联。然后通过将估计的非线性与其反向补偿估计的非线性来设计预测控制系统。仿真结果表明,所提出的非线性模型预测控制机制在线性预测控制对应上的运行时已经显着提高了运行时的性能和资源管理。

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