Cloud Computing Load Prediction in Double-channel Residual Self-attention Temporal Convolutional Network with Weight Adaptive Updating

Abstract

Resource load prediction is a prominent challenge issue with the widespread adoption of cloud computing. A novel cloud computing load prediction method has been proposed in Double-channel residual Self-attention Temporal convolutional Network with Weight adaptive updating (DSTNW). A Double-channel Temporal convolution Network model (DTN) has been developed. The double-channel dilated causal convolution has been adopted to replace a single channel dilated causal convolution in the DTN. A residual temporal self-attention mechanism has been proposed to improve the performance of network and focus on features with significant contributions from the DTN. Some errors for single and stacked Double-channel residual Self-attention Temporal convolutional Network (DSTN) have been evaluated. An adaptive weight strategy has been proposed to assign corresponding weights for the single and stacked DSTNs, respectively. Experimental results highlight that the developed method has outstanding prediction performance for cloud computing in comparison with some state-of-the-arts.