Microservice latency prediction model based on gated spatial-temporal convolutional networks
The multi-instance deployment of microservices and their interdependencies make it challenging to accurately capture the relationship between microservice invocation topology and service delay.To address this issue,a delay predic-tion model based on the gated attention spatial-temporal graph convolutional network(GaSTGCN)is proposed.By construc-ting graph data sequences of microservice invocation instances,a multi-layer spatial-temporal graph convolutional network is employed to capture temporal and spatial feature correlations of microservice nodes while considering the service invocation topology.Recognizing the increasing complexity of microservice application scales and the difficulty in capturing spatial-tem-poral correlations,the model integrates gated convolution mechanisms with dilated convolution techniques and adaptive ga-ting mechanisms to more accurately extract local and global microservice dependency features.Experimental results demon-strate that the proposed model achieves superior convergence performance and prediction accuracy compared to traditional prediction algorithms.
microserviceselastic expansion and contractiondelay predictiongated spatial-temporal convolution
万方数据
