Skip to main content
SpringerLink
Log in

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

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

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.

References

  1. Pang G, Shen C, Cao L, Hengel AVD (2021) Deep learning for anomaly detection: a review. ACM Comput Surv CSUR 54(2):1–38

    Google Scholar 

  2. Xin R, Chen P, Zhao Z (2023) CausalRCA: causal inference based precise fine-grained root cause localization for microservice applications. J Syst Softw 203:111724

    Article  Google Scholar 

  3. Bashar MA, Nayak R (2020) TAnoGAN: time series anomaly detection with generative adversarial networks. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp 1778–1785

  4. Kim H, Shon T (2022) Industrial network-based behavioral anomaly detection in AI-enabled smart manufacturing. J Supercomput 78(11):13554–13563

    Article  Google Scholar 

  5. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Statistics 1050:10

    Google Scholar 

  6. Jabbar A, Li X, Omar B (2021) A survey on generative adversarial networks: variants, applications, and training. ACM Comput Surv CSUR 54(8):1–49

    Google Scholar 

  7. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: International Conference on Machine Learning. PMLR, pp 214–223

  8. Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of Wasserstein GANs. Adv Neural Inf Process Syst 30

  9. Li D, Chen D, Jin B, Shi L, Goh J, Ng S-K (2019) MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks. In: International Conference on Artificial Neural Networks. Springer, pp 703–716

  10. Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2(4):433–459

    Article  Google Scholar 

  11. Angiulli F, Pizzuti C (2002) Fast outlier detection in high dimensional spaces. In: European Conference on Principles of Data Mining and Knowledge Discovery. Springer, pp 15–27

  12. Breunig MM, Kriegel H-P, Ng RT, Sander J (2000) Lof: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp 93–104

  13. Schölkopf B, Williamson RC, Smola A, Shawe-Taylor J, Platt J (1999) Support vector method for novelty detection. Adv Neural Inf Process Syst 12

  14. Schubert E, Sander J, Ester M, Kriegel HP, Xu X (2017) DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans Database Syst TODS 42(3):1–21

    Article  MathSciNet  Google Scholar 

  15. Liu FT, Ting KM, Zhou Z-H (2008) Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining. IEEE, pp 413–422

  16. Çavdar T, Ebrahimpour N, Kakız MT, Günay FB (2023) Decision-making for the anomalies in IIoTs based on 1D convolutional neural networks and Dempster–Shafer theory (DS-1DCNN). J Supercomput 79(2):1683–1704

    Article  Google Scholar 

  17. Hundman K, Constantinou V, Laporte C, Colwell I, Soderstrom T (2018) Detecting spacecraft anomalies using LSTMS and nonparametric dynamic thresholding. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 387–395

  18. Park S, Jung S, Jung S, Rho S, Hwang E (2021) Sliding window-based LightGBM model for electric load forecasting using anomaly repair. J Supercomput 77:12857–12878

    Article  Google Scholar 

  19. Su Y, Zhao Y, Niu C, Liu R, Sun W, Pei D (2019) Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2828–2837

  20. Malhotra P, Vig L, Shroff G, Agarwal P et al (2015) Long short term memory networks for anomaly detection in time series. In: Proceedings, vol 89, pp 89–94

  21. Kingma DP, Welling M (2013) Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114

  22. Zong B, Song Q, Min MR, Cheng W, Lumezanu C, Cho D, Chen H (2018) Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations

  23. Park D, Hoshi Y, Kemp CC (2018) A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder. IEEE Robot Autom Lett 3(3):1544–1551

    Article  Google Scholar 

  24. Audibert J, Michiardi P, Guyard F, Marti S, Zuluaga MA (2020) USAD: unsupervised anomaly detection on multivariate time series. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 3395–3404

  25. Chen P, Xia Y, Pang S, Li J (2015) A probabilistic model for performance analysis of cloud infrastructures. Concurr Comput Pract Exp 27(17):4784–4796

    Article  Google Scholar 

  26. Song Y, Xin R, Chen P, Zhang R, Chen J, Zhao Z (2023) Identifying performance anomalies in fluctuating cloud environments: a robust correlative-GNN-based explainable approach. Futur Gener Comput Syst 145:77–86

    Article  Google Scholar 

  27. Wen P, Yang Z, Wu L, Qi S, Chen J, Chen P (2022) A novel convolutional adversarial framework for multivariate time series anomaly detection and explanation in cloud environment. Appl Sci 12(20):10390

    Article  Google Scholar 

  28. Chen P, Liu H, Xin R, Carval T, Zhao J, Xia Y, Zhao Z (2022) Effectively detecting operational anomalies in large-scale IoT data infrastructures by using a GAN-based predictive model. Comput J

  29. Zhao H, Wang Y, Duan J, Huang C, Cao D, Tong Y, Xu B, Bai J, Tong J, Zhang Q (2020) Multivariate time-series anomaly detection via graph attention network. In: 2020 IEEE International Conference on Data Mining (ICDM). IEEE, pp 841–850

  30. Dos Santos C, Gatti M (2014) Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp 69–78

  31. Abdulaal A, Liu Z, Lancewicki T (2021) Practical approach to asynchronous multivariate time series anomaly detection and localization. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp 2485–2494

Download references

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.

Author information

Author notes

  1. Sibo Qi and Juan Chen have contributed to the work equally and should be regarded as co-first authors.

Authors and Affiliations

  1. School of Computer and Software Engineering, Xihua University, Chengdu, China

    Sibo Qi, Juan Chen, Peng Chen, Peian Wen & Xianhua Niu

  2. School of Emergency Management, Xihua University, Chengdu, China

    Lei Xu

Authors
  1. Sibo Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peian Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xianhua Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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.

Corresponding author

Correspondence to Peng Chen.

Ethics declarations

Conflict of interest

The authors declare no potential conflict of interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05534-3

Keywords

Search

Navigation