基于改进Informer的云计算资源负载预测OA北大核心CSTPCD

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.