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Improving accuracy of host load predictions on computational grids by artificial neural networks

机译:通过人工神经网络提高计算网格上主机负荷预测的准确性

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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.
机译: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。本文试图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。我们描述了在动态环境中提出的预测变量的可行性,并使用收集到的负载轨迹进行实验评估。结果表明,神经网络以令人惊讶的低开销实现了持续的性能改进。与先前提出的最佳方法相比,典型的20:10:1网络分别将预测误差的平均偏差和标准偏差分别降低了约60%和70%。培训和测试时间非常短,因为该网络仅需几秒钟即可对100,000多个样本进行培训,以便在一秒钟之内做出数以万计的准确预测。

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