Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networks

TL;DR

This paper introduces a spatiotemporal graph neural network model for accurately forecasting distributed system performance, capturing complex dependencies and dynamic changes over time.

Contribution

It presents a novel end-to-end framework combining graph convolution and gated recurrent networks for multi-level service performance prediction.

Findings

Outperforms existing methods on key metrics like MAE, RMSE, R2

Maintains robustness under different load conditions

Effectively models non-stationary temporal sequences

Abstract

This paper proposes a spatiotemporal graph neural network-based performance prediction algorithm to address the challenge of forecasting performance fluctuations in distributed backend systems with multi-level service call structures. The method abstracts system states at different time slices into a sequence of graph structures. It integrates the runtime features of service nodes with the invocation relationships among services to construct a unified spatiotemporal modeling framework. The model first applies a graph convolutional network to extract high-order dependency information from the service topology. Then it uses a gated recurrent network to capture the dynamic evolution of performance metrics over time. A time encoding mechanism is also introduced to enhance the model's ability to represent non-stationary temporal sequences. The architecture is trained in an end-to-end manner, optimizing the multi-layer nested structure to achieve high-precision regression of future service performance metrics. To validate the effectiveness of the proposed method, a large-scale public cluster dataset is used. A series of multi-dimensional experiments are designed, including variations in time windows and concurrent load levels. These experiments comprehensively evaluate the model's predictive performance and stability. The experimental results show that the proposed model outperforms existing representative methods across key metrics such as MAE, RMSE, and R2. It maintains strong robustness under varying load intensities and structural complexities. These results demonstrate the model's practical potential for backend service performance management tasks.