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Prediction in Projection: Computer Performance Forecasting, a Dynamical Systems Approach

机译:预测中的预测:计算机性能预测,一种动态系统方法

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摘要

Recent work in the field of dynamical systems provides evidence that computer systems are nonlinear-deterministic dynamical systems. This implies the existence of a deterministic update rule, which, in turn, implies the existence of a deterministic forecasting rule for the state variables of a running computer. Even a short-term prediction of these quantities, if accurate, could be effective in tailoring system resources on-the-fly to the dynamics of a computing application. For example, a good prediction of processor load could allow a computer to increase its energy efficiency by dynamically turning off unused CPUs, and then turning them back on based on the programs predicted needs. To explore this, I use a custom measurement infrastructure, delay-coordinate embedding and nonlinear time-series analysis to forecast processor load and cache performance of a set of simple C programs running on an Intel Core2 Duo. This proved to be quite effective. However, the use of traditional embedding techniques `on the flyu27 is impractical due to the time required to correctly perform the processing and post-processing of the data. My alternative to this is to use arbitrary low-dimensional projections. While this is not consistent with the requirements in the current literature, recent work by Mischaikow suggests that this alternative might work. I verified this conjecture, showing that forecasts based on two-dimensional projections are largely as effective as strategies that use the full embedded dynamics. This is in contrast to the current view in the nonlinear dynamics community that a one-to-one delay map is sufficient for successful prediction using delay coordinate embedding. My results suggest that this may not be a necessary condition. The success of the projection-based forecasting schemes brings into questions the need for full topological conjugacy in forecasting schema. The results presented here suggest ways of improving computer design at a systems level; they also provide evidence to support the use of semi-conjugacies in forecasting schemes.
机译:动力系统领域的最新工作提供了证据,证明计算机系统是非线性确定性动力系统。这意味着存在确定性更新规则,而更新规则又意味着对运行中的计算机的状态变量存在确定性预测规则。即使是对这些数量的短期预测(如果准确),也可能会有效地实时调整系统资源以适应计算应用程序的动态变化。例如,对处理器负载的良好预测可以允许计算机通过动态关闭未使用的CPU,然后根据预测的程序将其重新打开来提高其能源效率。为了探索这一点,我使用了自定义的测量基础架构,延迟坐标嵌入和非线性时间序列分析,以预测在Intel Core2 Duo上运行的一组简单C程序的处理器负载和缓存性能。事实证明这是非常有效的。但是,由于要正确执行数据的处理和后处理需要一定的时间,因此无法即时使用传统的嵌入技术。我的替代方法是使用任意的低维投影。尽管这与当前文献中的要求不一致,但是Mischaikow的最新工作表明这种替代方法可能有效。我验证了这个推测,表明基于二维投影的预测与使用完全嵌入式动态的策略在很大程度上一样有效。这与非线性动力学社区中的当前观点相反,一对一延迟图足以使用延迟坐标嵌入进行成功预测。我的结果表明这可能不是必要条件。基于投影的预测方案的成功使人们对预测方案中是否需要完全拓扑共轭提出了疑问。这里介绍的结果提出了在系统级别上改​​进计算机设计的方法。它们还提供了证据来支持在预测方案中使用半缀合物。

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    Garland Joshua T;

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  • 年度 2011
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