DeepPerf: Performance Prediction for Configurable Software with Deep Sparse Neural Network
DeepPerf: Performance Prediction for Configurable Software with Deep Sparse Neural Network
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DOI：
10.1109/icse.2019.00113
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发表时间：
2019-05
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影响因子：
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通讯作者：
Huong Ha;Hongyu Zhang
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文献类型：
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作者：
Huong Ha;Hongyu Zhang
许多软件系统为用户提供了一组配置选项，不同的配置可能会导致系统的运行时性能不同。由于配置的组合可能是指数级的，因此很难在所有可能的配置下详尽地部署和测量系统性能。最近，已经提出了几种学习方法来基于从小样本配置中收集的性能数据来建立性能预测模型，然后使用该模型来预测新配置下的系统性能。本文提出了一种将深度前向神经网络(FNN)与稀疏正则化技术(如L1正则化)相结合的高可配置软件系统建模方法。此外，我们还设计了一种实用的搜索策略来高效地自动调整网络超参数。我们的方法，称为DeepPerf，可以用比最先进的方法更少的训练数据以更高的预测精度预测具有二进制和/或数字配置选项的高度可配置的软件系统的性能值。在11个公开的真实世界数据集上的实验结果证实了该方法的有效性。
Many software systems provide users with a set of configuration options and different configurations may lead to different runtime performance of the system. As the combination of configurations could be exponential, it is difficult to exhaustively deploy and measure system performance under all possible configurations. Recently, several learning methods have been proposed to build a performance prediction model based on performance data collected from a small sample of configurations, and then use the model to predict system performance under a new configuration. In this paper, we propose a novel approach to model highly configurable software system using a deep feedforward neural network (FNN) combined with a sparsity regularization technique, e.g. the L1 regularization. Besides, we also design a practical search strategy for automatically tuning the network hyperparameters efficiently. Our method, called DeepPerf, can predict performance values of highly configurable software systems with binary and/or numeric configuration options at much higher prediction accuracy with less training data than the state-of-the art approaches. Experimental results on eleven public real-world datasets confirm the effectiveness of our approach.