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An efficient GAN-based predictive framework for multivariate time series anomaly prediction in cloud data centers

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Abstract

Recently, a growing amount of time series data has been collected in cloud data centers, making anomaly detection for multivariate time series analysis increasingly necessary. However, extracting meaningful features from multivariate time series remains challenging due to the limited amount of labeled data and highly complex temporal correlations. Additionally, many unsupervised deep learning methods often result in a high false alarm rate. This study proposes a new unsupervised multivariate time series anomaly prediction model called the Predictive Wasserstein Generative Adversarial Network with Gradient Penalty (PW-GAN-GP). Our model adopts both Wasserstein Distance and Gradient Penalty, making the adversarial training more stable and helping the generator’s output to more closely resemble the real data. Moreover, a novel anomaly score function combining reconstruction, discrimination, and prediction errors is used to improve precision while maintaining recall. The experimental results on four public cloud computing datasets demonstrate that our proposed PW-GAN-GP outperforms the suboptimal baseline, with improvements of 22.11% and 13.47% in precision and F1 scores, respectively.

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Data availability

The authors confirm that the data (Server Machine Dataset, Pooled Server Metrics and NASA Dataset) supporting this study’s findings are publicly available at https://github.com/smallcowbaby/OmniAnomaly, https://github.com/eBay/RANSynCoders and https://github.com/khundman/telemanom.

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Funding

This research was funded by the Science and Technology Program of Sichuan Province under Grant Nos. 2020JDRC0067, 2021JDR0222, and 2020YFG0326, and the Talent Program of Xihua University under Grant Nos. Z202047 and Z222001.

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SQ contributed to methodology, conceptualization, investigation, software, visualization, and writing—original draft. JC contributed to methodology, supervision, and writing—review and editing. PC contributed to methodology, investigation, and conceptualization. PW contributed to investigation and validation. XN contributed to investigation and methodology. LX contributed to methodology and conceptualization.

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Correspondence to Peng Chen.

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Qi, S., Chen, J., Chen, P. et al. An efficient GAN-based predictive framework for multivariate time series anomaly prediction in cloud data centers. J Supercomput 80, 1268–1293 (2024). https://doi.org/10.1007/s11227-023-05534-3

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