Key performance indicators anomaly detection method based on deep spatio-temporal fusion

Songlin Yang; Jing Li; Xudong He; Weizhou Peng; Ying Zhu; Rongbin Gu; Yunlong Zhu; Jun Huang

doi:10.1117/12.3035393

19 July 2024 Key performance indicators anomaly detection method based on deep spatio-temporal fusion

Songlin Yang, Jing Li, Xudong He, Weizhou Peng, Ying Zhu, Rongbin Gu, Yunlong Zhu, Jun Huang

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 1321325 (2024) https://doi.org/10.1117/12.3035393
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

The detection of anomalies in Key Performance Indicators (KPIs) is fundamental to the operation and maintenance of microservices. The spatio-temporal interaction feature show how time and space relate in a specific context, which reveals KPI data change patterns. However, most existing studies concentrate on the spatial and temporal dependencies of KPI data, neglecting the extraction of spatio-temporal interaction features. In this paper, we propose a Deep spatiotemporal Fusion-based KPI Anomaly Detection method, DFAD, which constructs spatio-temporal interaction features by correlating spatial and temporal features. First, the model utilizes parallel spatio-temporal encoders to capture various temporal and spatial features through convolution-attention. Second, a set of parallel spatio-temporal decoders are designed to independently handle decoding while merging complementary features using cross-attention. Lastly, features are combined from a spatial viewpoint using channel-attention, enhancing the fusion effect. We conducts comparison experiments on four public datasets with various state-of-the-art methods and the results confirm that DFAD enhances detection performance compared to mainstream methods, validating the effectiveness of this approach.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Songlin Yang, Jing Li, Xudong He, Weizhou Peng, Ying Zhu, Rongbin Gu, Yunlong Zhu, and Jun Huang "Key performance indicators anomaly detection method based on deep spatio-temporal fusion", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 1321325 (19 July 2024); https://doi.org/10.1117/12.3035393

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