Microservice fault detection algorithm based on spatial-temporal convolutional network

Meng Zhang; Zihao Chen; Fangfang Yuan; Luqiang Tian

doi:10.1117/12.3009452

11 October 2023 Microservice fault detection algorithm based on spatial-temporal convolutional network

Meng Zhang, Zihao Chen, Fangfang Yuan, Luqiang Tian

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 129180Z (2023) https://doi.org/10.1117/12.3009452
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

Microservice architecture is a new architectural pattern for deploying applications and services in the cloud, which aims to split a large service into a series of different and independent microservices, and provide users with better software design services. However, with the increasing business demand and the continuous expansion of the scale of the microservice system, the complexity of the system itself and the complexity of the network relationships make fault detection difficult. From the perspective of microservice node status and dependencies, we propose a novel fault detection algorithm based on a spatial-temporal convolutional network. It uses a graph convolutional network to effectively capture the complex spatial dependencies of microservice network relationships and constructs a temporal convolutional attention module, which uses dilated causal convolution, residual block and attention mechanism to better control the memory length of the model, obtain the key features in the time series, and improve the accuracy of the model. Finally, a large number of experiments prove the effectiveness of the algorithm in microservice fault detection.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Meng Zhang, Zihao Chen, Fangfang Yuan, and Luqiang Tian "Microservice fault detection algorithm based on spatial-temporal convolutional network", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 129180Z (11 October 2023); https://doi.org/10.1117/12.3009452

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KEYWORDS

Data modeling

Detection and tracking algorithms

Machine learning

Education and training

Convolution

Autoregressive models

Matrices

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