摘要：

负载预测是云计算资源管理中的重要组成部分，准确预测云资源的使用情况可提高云平台性能及防止资源浪费，然而云计算资源使用的动态性和不确定性使得负载预测较为困难，尽管Informer在时序预测领域取得了较好的效果，但未对时间的因果依赖关系加以限制造成未来信息泄露，也未考虑网络深度的增加导致模型性能下降的问题。为解决上述问题，提出一种基于改进Informer的多步负载预测模型(Informer-DCR)。将编码器中各注意力块之间的正则卷积替换为扩张因果卷积，使深层网络中的高层能够接收更大范围的输入信息来提高模型预测精度，并保证时序预测过程的因果性。在编码器中添加残差连接，使网络中低层的输入信息直接传到后续的高层，解决了深层网络退化问题。实验结果表明，Informer-DCR模型在不同预测步长下的平均绝对误差比Informer、时间卷积网络等主流预测模型降低了8.4%~40.0%，并且在训练过程中表现出比Informer更好的收敛性。

关键词: 云计算, 负载预测, Informer模型, 扩张因果卷积, 残差连接

Abstract:

Load prediction is an essential part of cloud computing resource management. Accurate prediction of cloud resource usage can improve cloud platform performance and prevent resource wastage. However, the dynamic and mutative use of cloud computing resources makes load prediction difficult, and managers cannot allocate resources reasonably. In addition, although Informer has achieved better results in time-series prediction, it does not impose restrictions on the causal dependence of time, causing future information leakage. Moreover, it does not consider the increase in network depth leading to model performance degradation. A multi-step load prediction model based on an improved Informer, known as Informer-DCR, is proposed. The regular convolution between attention blocks in the encoder is replaced by dilated causal convolution, such that the upper layer in the deep network can receive a wider range of input information to improve the prediction accuracy of the model, and ensure the causality of the time-series prediction process. Simultaneously, the residual connection is added to the encoder, such that the input information of the lower layer of the network is directly transmitted to the subsequent higher layer, and the deep network degradation is solved to improve the model performance. The experimental results demonstrate that compared with the mainstream prediction models such as Informer and Temporal Convolutional Network(TCN), the Mean Absolute Error(MAE) of the Informer-DCR model is reduced by 8.4%-40.0% under different prediction steps, and Informer-DCR exhibits better convergence than Informer during the training process.

Key words: cloud computing, load prediction, Informer model, dilated casual convolution, residual connection