Abstract
A Software Engineering Manager (EM) has to cater to the demand for higher reliability and resilience in Production while simultaneously addressing the evolution of software architecture from monolithic applications to multi-cloud distributed microservices. Pre-release functional testing is no longer sufficient to eliminate faults as more and more issues are generated at runtime, which is challenging to diagnose due to complex inter-service dependencies and dynamic late binding of services. Bugs in Production are known to propagate across software components and become critical as they go undetected.
This paper introduces LogAttention, a methodology based on analysis of runtime logs that provides actionable insights to the EM to identify faults and preempt failure in Production. LogAttention is a Log Anomaly Detection (LAD) technique that uses Attention-based Transformer Models to identify Anomalous Log Messages. LogAttention assigns a quality score to the software release in Production and presents remarkable logs to the EM to analyze, predict, and preempt failure. This paper presents empirical evidence showing that LogAttention outperforms existing LAD techniques to identify anomalous log messages and ensure that the detected log anomalies are reliable indicators of the health of a software release.
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References
Di Francesco, P., Malavolta, I., Lago, P.: Research on architecting microservices: trends, focus, and potential for industrial adoption. In: ICSA 2017 (2017)
Mariani, L., et al.: Localizing faults in cloud systems. In: IEEE ICST 2018 (2018)
Wu, L., Tordsson, J., Elmroth, E., Kao, O.: MicroRCA: root cause localization of performance issues in microservices. In: IEEE/IFIP NOMS 2020 (2020)
He, P., Zhu, J., He, S., Li, J., Lyu, M.R.: An evaluation study on log parsing and its use in log mining. In: IEEE/IFIP DSN 2016 (2016)
Nedelkoski, S., Bogatinovski, J., Acker, A., Cardoso, J., Kao, O.: Self-supervised log parsing. In: Dong, Y., Mladenić, D., Saunders, C. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12460, pp. 122–138. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67667-4_8
Cheng, J., Dong, L., Lapata, M.: Long short-term memory-networks for machine-reading
Vaswani, A., et al.: Attention is all you need (2017)
Klosowski, P.: Deep learning for NLP and language modeling. In: SPA 2018 (2018)
Clark, K., Luong, M., Le, Q.V., Manning, C.D.: ELECTRA: Pre-training text encoders as discriminators rather than generators (2020)
Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: Smaller, faster, cheaper, and lighter. In: Co-Located with NeurIPS 2019, 5th edn. (2019)
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in NLP (EMNLP) (2014)
Vellido, A., Gibert, K., Angulo, C., Martín Guerrero, J.D. (eds.): WSOM 2019. AISC, vol. 976. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-19642-4
Liu, F.T., Ting, K.M., Zhou, Z.: Isolation forest. In: Paper presented at the Eighth IEEE International Conference on Data Mining, pp. 413–422 (2008)
Kazerouni, A., et al.: Active Learning for Skewed Data Sets (2020)
Liu, Y., et al.: RoBERTa: A robustly optimized BERT pretraining approach (2019)
Du, M., Li, F., Zheng, G., Srikumar, V.: DeepLog: anomaly detection and diagnosis from System Logs through deep learning. In: CCS 2017 (2017)
Meng, W., et al.: LogAnomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs (2019)
Zhang, X., et al.: Robust log-based anomaly detection on unstable log data (2019)
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Munir, S., Ali, H., Qureshi, J. (2022). Log Attention – Assessing Software Releases with Attention-Based Log Anomaly Detection. In: Hacid, H., et al. Service-Oriented Computing – ICSOC 2021 Workshops. ICSOC 2021. Lecture Notes in Computer Science, vol 13236. Springer, Cham. https://doi.org/10.1007/978-3-031-14135-5_11
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DOI: https://doi.org/10.1007/978-3-031-14135-5_11
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