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Learning-aided predictor integration for system performance prediction

机译：学习辅助预测器集成，用于系统性能预测

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摘要

The integration of multiple predictors promises higher prediction accuracy than the accuracy that can be obtained with a single predictor. The challenge is how to select the best predictor at any given moment. Traditionally, multiple predictors are run in parallel and the one that generates the best result is selected for prediction. In this paper, we propose a novel approach for predictor integration based on the learning of historical predictions. Compared with the traditional approach, it does not require running all the predictors simultaneously. Instead, it uses classification algorithms such as k-Nearest Neighbor (k-NN) and Bayesian classification and dimension reduction technique such as Principal Component Analysis (PCA) to forecast the best predictor for the workload under study based on the learning of historical predictions. Then only the forecasted best predictor is run for prediction. Our experimental results show that it achieved 20.18% higher best predictor forecasting accuracy than the cumulative MSE based predictor selection approach used in the popular Network Weather Service system. In addition, it outperformed the observed most accurate single predictor in the pool for 44.23% of the performance traces.
机译：多个预测器的集成保证了比单个预测器可获得的更高的预测精度。面临的挑战是如何在任何给定时刻选择最佳预测变量。传统上，多个预测器并行运行，并选择产生最佳结果的预测器进行预测。在本文中，我们基于历史预测的学习提出了一种新的预测器集成方法。与传统方法相比，它不需要同时运行所有预测变量。取而代之的是，它使用分类算法（例如k最近邻（k-NN）和贝叶斯分类）和降维技术（例如主成分分析（PCA））在学习历史预测的基础上预测所研究工作量的最佳预测器。然后，仅运行预测的最佳预测变量进行预测。我们的实验结果表明，与流行的网络天气服务系统中使用的基于MSE的累积预测器选择方法相比，它的最佳预测器预测准确性提高了20.18％。此外，在性能曲线中，它的性能优于集合中观察到的最准确的单个预测变量。

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