基于长-短时序特征融合的资源负载预测模型

高准确率的资源负载预测能够为实时任务调度提供依据，从而降低能源消耗。但是，针对资源负载的时间序列的预测模型，大多是通过提取时间序列的长时序依赖特性来进行短期或者长期预测，忽略了时间序列中的短时序依赖特性。为了更好地对资源负载进行长期预测，提出了一种基于长-短时序特征融合的边缘计算资源负载预测模型。首先，利用格拉姆角场（GAF）将时间序列转变为图像格式数据，以便利用卷积神经网络（CNN）来提取特征；然后，通过卷积神经网络提取空间特征和短期数据的特征，用长短期记忆（LSTM）网络来提取时间序列的长时序依赖特征；最后，将所提取的长、短时序依赖特征通过双通道进行融合，从而实现长期资源负载预测。实验结果表明，所提出的模型在阿里云集群跟踪数据集CPU资源负载预测中的平均绝对误差（MAE）为3.823，均方根误差（RMSE）为5.274，拟合度（R²）为0.815 8，相较于单通道的CNN和LSTM模型、双通道CNN+LSTM和ConvLSTM+LSTM模型，以及资源负载预测模型LSTM-ED和XGBoost，所提模型的预测准确率更高。

Resource load prediction model based on long-short time series feature fusion

Resource load prediction with high accuracy can provide a basis for real-time task scheduling， thus reducing energy consumption. However， most prediction models for time series of resource load make short-term or long-term prediction by extracting the long-time series dependence characteristics of time series and neglecting the short-time series dependence characteristics of time series. In order to make a better long-term prediction of resource load， a new edge computing resource load prediction model based on long-short time series feature fusion was proposed. Firstly， the Gram Angle Field （GAF） was used to transform time series into image format data， so as to extract features by Convolutional Neural Network （CNN）. Then， the CNN was used to extract spatial features and short-term data features， the Long Short-Term Memory （LSTM） network was used to extract the long-term time series dependent features of time series. Finally， the extracted long-term and short-term time series dependent features were fused through dual-channel to realize long-term resource load prediction. Experimental results show that， the Mean Absolute Error （MAE）， Root Mean Square Error （RMSE） and R-squared（R²） of the proposed model for CPU resource load prediction in Alibaba cloud clustering tracking dataset are 3.823， 5.274， and 0.815 8 respectively. Compared with the single-channel CNN and LSTM models， dual-channel CNN+LSTM and ConvLSTM+LSTM models， and resource load prediction models such as LSTM Encoder-Decoder （LSTM-ED） and XGBoost， the proposed model can provide higher prediction accuracy.