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    • 7. 发明授权
    • Self-modulation in a model-based automated management framework
    • 基于模型的自动化管理框架中的自调制
    • US07444272B2
    • 2008-10-28
    • US11250066
    • 2005-10-13
    • Guillermo A. AlvarezLinda M. DuyanovichJohn D. PalmerSandeep M. UttamchandaniLi Yin
    • Guillermo A. AlvarezLinda M. DuyanovichJohn D. PalmerSandeep M. UttamchandaniLi Yin
    • G06F17/10G06E1/00
    • G05B17/02G05B13/042
    • Embodiments herein present a method, system, computer program product, etc. for automated management using a hybrid of prediction models and feedback-based systems. The method begins by calculating confidence values of models. Next, the method selects a first model based on the confidence values and processes the first model through a constraint solver to produce first workload throttling values. Following this, workloads are repeatedly processed through a feedback-based execution engine, wherein the feedback-based execution engine is controlled by the first workload throttling values. The first workload throttling values are applied incrementally to the feedback-based execution engine, during repetitions of the processing of the workloads, with a step-size that is proportional to the confidence values. The processing of the workloads is repeated until an objective function is maximized, wherein the objective function specifies performance goals of the workloads.
    • 本文的实施例提出了使用预测模型和基于反馈的系统的混合的自动化管理的方法,系统,计算机程序产品等。 该方法从计算模型的置信度开始。 接下来,该方法基于置信度值选择第一模型,并通过约束求解器处理第一模型以产生第一工作负载节流值。 此后,通过基于反馈的执行引擎重复处理工作负载,其中基于反馈的执行引擎由第一工作负载节流值控制。 在重复处理工作负载期间,第一个工作负载限制值将逐步应用于基于反馈的执行引擎,步长与置信度值成比例。 重复处理工作负载直到目标函数最大化,其中目标函数指定工作负载的性能目标。