Cloud Resource Demand Prediction Based on VMD-GRU-TCN Hybrid Model

With the rapid development of cloud computing and data centers, the demand for cloud computing resources is constantly changing, and resource allocation has gradually become one of the most challenging problems in the cloud environment. Oversupply increases energy waste and costs, while undersupply, on the other hand, leads to violations of Service Level Agreements(SLAs) and reduces Quality of Service(QoS). Therefore, in order to cope with the changing cloud resource requirements, resource prediction models are required to provide reasonable accuracy. Traditional statistical models are simple in structure, and most of them can only deal with stationary series. In view of the relatively complex sequence of resource occupation in the cloud environment, which is nonlinear, non-stationary, noisy, and uncertain, it is difficult to capture its stable state. In this paper, we adopt a neural network model to solve this problem. First, the sequence is decomposed by variational mode decomposition(VMD), and multiple sub-sequences are preprocessed by sliding windows. The Gated Recurrent Unit(GRU) based on attention mechanism is used to preliminarily obtain the data characteristic information, and global features are extracted with Temporal Convolutional Network(TCN) by dilation convolution. Then, the comprehensive feature information extracted from the two networks with different structures is fused to predict the resource occupancy rate at a future time. For three datasets with different characteristics, the model reduces the value of the MAPE indicator by 0.442%, 4.764% and 2.331%. Experimental results show that the proposed method improves the accuracy of resource prediction, and is superior to existing models in MAPE, RMSE, and R2 evaluation criteria.