Improving accuracy of host load predictions on computational grids by artificial neural networks
Improving accuracy of host load predictions on computational grids by artificial neural networks
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DOI：
10.1080/17445760.2010.481786
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发表时间：
2009-05
期刊：
影响因子：
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通讯作者：
T. V. Duy;Yukinori Sato;Y. Inoguchi
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文献类型：
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作者：
T. V. Duy;Yukinori Sato;Y. Inoguchi
预测系统主机负载的能力对于计算网格有效利用共享资源非常重要。本文尝试通过应用神经网络预测器来提高主机负载预测的准确性，以达到最佳性能和负载平衡的目标。我们描述了所提出的预测器在动态环境中的可行性，并使用收集的负载轨迹进行实验评估。结果表明，神经网络以惊人的低开销实现了一致的性能改进。与之前提出的最佳方法相比，典型的 20:10:1 网络将预测误差的平均值和标准偏差分别降低了约 60% 和 70%。训练和测试时间极短，因为该网络只需要几秒钟就可以用超过 100,000 个样本进行训练，以便在一秒钟内做出数万个准确预测。
The capability to predict the host load of a system is significant for computational grids to make efficient use of shared resources. This paper attempts to improve the accuracy of host load predictions by applying a neural network predictor to reach the goal of best performance and load balance. We describe feasibility of the proposed predictor in a dynamic environment, and perform experimental evaluation using collected load traces. The results show that the neural network achieves a consistent performance improvement with surprisingly low overhead. Compared with the best previously proposed method, the typical 20:10:1 network reduces the mean and standard deviation of the prediction errors by approximately 60% and 70%, respectively. The training and testing time is extremely low, as this network needs only a couple of seconds to be trained with more than 100,000 samples in order to make tens of thousands of accurate predictions within just a second.