Citation:

Rajeev Kumar Sharma, 2025. "AI-Augmented Log Analysis for Predictive Maintenance in Distributed Java Applications", ESP Journal of Engineering & Technology Advancements  5(2): 315-319.

Abstract:

Thanks to AI-based log analysis in JVM, predictive maintenance is making support proactive, thus catching potential system failures early and raising reliability while reducing time spent offline. This review analyses the best computer science methods for parsing Java logs, choosing important parts from the logs and detecting anomalies. Logs that are not already in a standard format are parsed and turned into useful templates for straightforward analysis. Employing sequence embeddings and graph representations makes it easier to predict system behavior from log events. LSTM, BiLSTM and Transformer anomaly detection and prediction models are tested and results show that Transformer provides the highest accuracy of the three, whereas BiLSTM and LSTM show better trade-offs. Top issues identified are that log formats vary greatly for different Java frameworks, connecting AI models with existing log systems can be difficult and it is tough to handle quick anomaly detection in high-traffic applications. There are promising future ideas to use graph neural networks to detect event links, apply federated learning across organizations without letting them share logs, practice causal inference for exact analysis and progress in developing systems that fix problems automatically according to predictions. To maintain strong performance in systems that keep changing, models need to be interpretable and capable of learning all the time. This paper outlines how to reach complete, accurate and scalable usage of log-based predictive maintenance tools in Java ecosystems.

References:

[1] He S., He P., Chen Z., Yang T., Su Y., Lyu M. R., “A survey on automated log analysis for reliability engineering”, ACM Computing Surveys, 2021, 54(6), 1–37.

[2] Wang J., Li C., Han S., Sarkar S., Zhou X., “Predictive maintenance based on event-log analysis: A case study”, IBM Journal of Research and Development, 2017, 61(1), 11:121–11:132.

[3] Jiang Z., Li T., Zhang Z., Ge J., You J., Li L., “A survey on log research of AIOps: Methods and trends”, Mobile Networks and Applications, 2021, 26(6), 2353–2364.

[4] Achouch M., Dimitrova M., Ziane K., Karganroudi S. S., Dhouib R., Ibrahim H., Adda M., “On predictive maintenance in Industry 4.0: Overview, models, and challenges”, Applied Sciences, 2022, 12(16), 8081.

[5] Du M., Li F., Zheng G., Srikumar V., Ambrose J., “DeepLog: Anomaly detection and diagnosis from system logs through deep learning”, Proceedings of the 2018 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018, pp. 1285–1294.

[6] He P., Zheng Y., Lyu M. R., “Experience report: System log analysis for anomaly detection”, Proceedings of the 2016 IEEE/ACM 13th Working Conference on Mining Software Repositories, 2016, pp. 172–183.

[7] Fu Q., Zheng Y., Lou J. G., Xie T., “LogRobust: Transactional log parsing under noise and format evolution”, Proceedings of the 2018 IEEE/ACM 15th International Conference on Mining Software Repositories, 2018, pp. 400–410.

[8] Meng W., Fu Q., Lau R. Y. K., Muntean G. M., “Abstracting log lines by system event patterns for log mining”, IEEE Transactions on Knowledge and Data Engineering, 2019, 31(2), 294–307.

[9] Le T. D., Kroh T., Avritzer A., Joshi J. B. D., “Online log parsing for scalable log analytics”, IEEE Transactions on Network and Service Management, 2020, 17(3), 1515–1528.

[10] Khan A., Hassan M. M., “Predictive maintenance for manufacturing cyber-physical systems using machine logs”, Journal of Manufacturing Systems, 2020, 57, 238–249.

[11] Xu J., Li Y., Zhang Q., “Integrating AI into ELK: AIOps for cloud infrastructure maintenance”, IBM Journal of Research and Development, 2021, 65(4/5), 1–12.

[12] Min S., Wang L., Zhao Y., “Real-time log anomaly detection on streaming data with LSTM networks”, IEEE Transactions on Industrial Informatics, 2022, 18(5), 3194–3204.

[13] Choi H., Kim S., “An MLOps framework for continuous deployment of log-analysis models in production”, Journal of Systems and Software, 2022, 187, 111213.

[14] Zhang Y., Liu J., Tang K., “A comprehensive benchmark dataset for log parsing and anomaly detection”, Data and Knowledge Engineering, 2024, 146, 102799.

[15] Sharma A., Gupta R., Singh P., “Empirical evaluation of log-based failure prediction models”, Journal of Software Reliability, 2023, 12(4), 210–225.

[16] Fu Q., Lou J. G., Wang H., “Robust log parsing for uncertain logs”, IEEE Transactions on Dependable and Secure Computing, 2016, 13(6), 733–747.

[17] Sricharan B., Shanmugam S., Sekhar C., “Graph-based log anomaly detection using deep learning”, Journal of Systems and Software, 2019, 158, 110–123.

[18] Pan X., Li Y., Gao T., Ling T., “Hierarchical failure prediction in distributed systems through log feature learning”, Journal of Software: Evolution and Process, 2022, 34(5), e2345.

[19] Xie L., Zhao J., Wang F., “End-to-end log-based predictive maintenance with Transformer models”, IEEE Transactions on Automation Science and Engineering, 2023, 20(2), 1023–1034.

[20] Li X., Wang Y., Zhou Z., “Benchmarking AI models for real-time log anomaly detection”, International Journal of Software Engineering, 2021, 29(2), 98–115.

[21] Zeng Q., Li H., Xu Y., “GraphLog: Graph neural network-based log anomaly detection in distributed systems”, IEEE Transactions on Network Science and Engineering, 2022, 9(1), 115–127.

[22] Kumar S., Patel R., Singh N., “Federated learning for cross-domain log analysis in cloud environments”, Journal of Cloud Computing, 2023, 12(3), 45–60.

[23] Lee D., Park J., Choi M., “Causal discovery in log data for root-cause analysis”, Information and Software Technology, 2021, 136, 106574.

[24] Perez L., Zhang T., Gómez P., “Towards self-healing microservice architectures: Automated remediation driven by predictive maintenance”, Journal of Systems Architecture, 2024, 127, 102548.

[25] Wang X., Chen L., “Explainable AI in log-based anomaly detection: Techniques and challenges”, ACM Computing Surveys, 2023, 56(2), 1–34.

Keywords:

AI-Augmented Log Analysis; Predictive Maintenance; Distributed Java Applications.