基于LSTM网络的移动云计算多元负载预测模型

针对移动云主机负载变化大、难以精准预测的问题，提出一种联合特征选择下基于长短期记忆网络的AR-LSTM-ED负载预测模型，能够对云主机负载进行单步和长时间多步预测。首先采用联合特征选择的方法得到与目标预测负载序列相关的其他负载序列，并且利用适用于在线预测的无抽取的小波变换方法将目标预测特征分解成更加易于预测的子序列。最后将这些序列和目标预测序列一起输入AR-LSTM-ED模型中，AR-LSTM-ED模型利用长短期记忆编-解码网络对目标负载进行预测，具有能够捕捉负载中的长期依赖关系的优点，且进一步结合了自回归模型（AR）以预测负载中的线性数据。在真实的Google云计算数据集上验证算法，对比实验结果表明，本文提出的方法取得了更好的性能。

Multi Feature Load Forecasting Model for LSTM Network in Mobile Cloud Computing

Aiming at the problem that the load of mobile virtual machine changes greatly and it is difficult to be predicted accurately, this paper proposed an AR-LSTM-ED load forecasting model based on joint feature selection, which can carry out single-step and multi-step prediction of cloud host load. In this paper, the joint feature selection method was used to obtain other load series related to the target prediction load series, and the undecimated wavelet transform method suitable for online prediction was used to decompose the target prediction features into subsequences which could be easier to be predicted. Finally, these sequences and target prediction sequences were input into AR-LSTM-ED model. The model used the long-short term memory encoder-decoder network to predict the target load, which could capture the long-term dependence. The autoregressive model (AR) was further combined to predict the linear data in the load. We used the Google cloud computing data set to verify the algorithm. The experimental results show that the proposed method achieves better performance.