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Resource usage prediction of cloud workloads using deep bidirectional long short term memory networks

机译:使用深双向长期内存网络的云工作负载的资源使用预测

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Resource usage prediction is an important aspect for achieving optimal resource provisioning in cloud. The presence of long range dependence in cloud workloads makes conventional time series resource usage prediction models unsuitable for prediction. In this paper, we proposed to use multivariate long short term memory (LSTM) models for prediction of resource usage in cloud workloads. We analyze and compare the predictions of LSTM model and bidirectional LSTM model with fractional difference based methods. The proposed LSTM models have been evaluated and compared with the state-of-the-art existing methods on Google cluster trace [1]. The experimental results show that the proposed algorithms outperform state-of-the-art algorithms.
机译:资源使用预测是实现云中最佳资源供应的一个重要方面。在云工作负载中存在长距离依赖性使得传统的时间序列资源使用预测模型不适合预测。在本文中,我们建议使用多变量的长短期内存(LSTM)模型来预测云工作负载中的资源使用情况。我们用基于分数差异的方法分析和比较LSTM模型和双向LSTM模型的预测。已经评估了所提出的LSTM模型,并将其与Google Cluster Trace上的最先进的现有方法进行了评估[1]。实验结果表明,所提出的算法优于最先进的算法。

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