AI-Driven Incident Detection and Automated Remediation for Cloud-Native Applications

  • Authors

    • Uday Allala Lead SRE Engineer, USA. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2020PII6X9M

    Published 09-08-2020

  • Cloud-Native Applications, Artificial Intelligence, Incident Detection, Automated Remediation, Anomaly Detection, AIOps, Kubernetes, Machine Learning, Predictive Analytics, Self-Healing Systems

    Issue

    Vol. 3 No. 2 (2020)

    Section

    Articles

  • Abstract

    Cloud-native applications are built with distributed micro services, containers, orchestration platforms and dynamic infrastructure and incident detection and recovery is becoming more complex. Traditional monitoring and incident management techniques typically rely on predefined thresholds and static rules, which can lead to late detection, excessive false alarms and inability to detect complex and evolving failures. This paper introduces an AI-based incident detection and automated remediation approach for cloud-native apps that combines real-time observability data, machine learning-based anomaly detection, intelligent incident classification, root cause analysis, and automated recovery. It constantly checks for abnormal behavior in the application logs, infrastructure metrics, distributed traces, container events, and service-level telemetry, to define the severity of the incident. Algorithms such as machine learning and deep learning have been added to enhance the ability to detect, and adaptively recognize, new incidents that have not yet been seen. The proposed framework also introduces an intelligent remediation pipeline which correlates detected incidents with identified causes and matches them to appropriate recovery steps such as container restart, service rescheduling, scaling of resources, configuration fix, and rollback of applications. The framework is assessed using different workloads and incident scenarios in a cloud-native experimental environment to compare against traditional rule-based and machine learning-based baseline environments. The accuracy, precision, recall, F1-score, ROC-AUC, detection latency, mean time to detect (MTTD), mean time to remediate (MTTR) and remediation success rate are used to measure the performance. The experimental assessment verifies how effective AI-driven detection and automated remediation can be in minimizing incident response time, enhance detection accuracy and operational resilience. The study brings an end-to-end approach for intelligent, adaptive and self-healing cloud-native operations.

  • References

    [1] Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., & Wilkes, J. (2015, April). Large-scale cluster management at Google with Borg. In Proceedings of the tenth european conference on computer systems (pp. 1-17).

    [2] Burns, B., Grant, B., Oppenheimer, D., Brewer, E., & Wilkes, J. (2016). Borg, omega, and kubernetes. Communications of the ACM, 59(5), 50-57.

    [3] Dragoni, N., Giallorenzo, S., Lafuente, A. L., Mazzara, M., Montesi, F., Mustafin, R., & Safina, L. (2017). Microservices: yesterday, today, and tomorrow. Present and ulterior software engineering, 195-216.

    [4] Villamizar, M., Garcés, O., Castro, H., Verano, M., Salamanca, L., Casallas, R., & Gil, S. (2015, September). Evaluating the monolithic and the microservice architecture pattern to deploy web applications in the cloud. In 2015 10th computing colombian conference (10ccc) (pp. 583-590). IEEE.

    [5] Hochenbaum, J., Vallis, O. S., & Kejariwal, A. (2017). Automatic anomaly detection in the cloud via statistical learning. arXiv preprint arXiv:1704.07706.

    [6] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58.

    [7] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000, May). LOF: identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data (pp. 93-104).

    [8] Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008, December). Isolation forest. In 2008 eighth ieee international conference on data mining (pp. 413-422). Ieee.

    [9] Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.

    [10] Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).

    [11] Graves, A. (2012). Long short-term memory. Supervised sequence labelling with recurrent neural networks, 37-45.

    [12] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30(1), 123-135.

    [13] Du, M., Li, F., Zheng, G., & Srikumar, V. (2017, October). Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In Proceedings of the 2017 ACM SIGSAC conference on computer and communications security (pp. 1285-1298).

    [14] He, P., Zhu, J., Zheng, Z., & Lyu, M. R. (2017, June). Drain: An online log parsing approach with fixed depth tree. In 2017 IEEE international conference on web services (ICWS) (pp. 33-40). IEEE.

    [15] Meng, W., Liu, Y., Zhu, Y., Zhang, S., Pei, D., Liu, Y., ... & Zhou, R. (2019, August). Loganomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs. In Ijcai (Vol. 19, No. 7, pp. 4739-4745).

    [16] Zhang, X., Xu, Y., Lin, Q., Qiao, B., Zhang, H., Dang, Y., ... & Zhang, D. (2019, August). Robust log-based anomaly detection on unstable log data. In Proceedings of the 2019 27th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering (pp. 807-817).

    [17] Wang, P., Xu, J., Ma, M., Lin, W., Pan, D., Wang, Y., & Chen, P. (2018, May). Cloudranger: Root cause identification for cloud native systems. In 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) (pp. 492-502). IEEE.

    [18] Wu, L., Tordsson, J., Elmroth, E., & Kao, O. (2020, April). Microrca: Root cause localization of performance issues in microservices. In IEEE/IFIP Network Operations and Management Symposium (NOMS).

    [19] Dang, Y., Lin, Q., & Huang, P. (2019, May). Aiops: real-world challenges and research innovations. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (pp. 4-5). IEEE.

    [20] Li, Y., Jiang, Z. M., Li, H., Hassan, A. E., He, C., Huang, R., ... & Chen, P. (2020). Predicting node failures in an ultra-large-scale cloud computing platform: an aiops solution. ACM Transactions on Software Engineering and Methodology (TOSEM), 29(2), 1-24.

    [21] Sigelman, B. H., Barroso, L. A., Burrows, M., Stephenson, P., Plakal, M., Beaver, D., ... & Shanbhag, C. (2010). Dapper, a large-scale distributed systems tracing infrastructure.

    [22] Dean, J., & Barroso, L. A. (2013). The tail at scale. Communications of the ACM, 56(2), 74-80.

    [23] Beyer, B., Jones, C., Petoff, J., & Murphy, N. R. (2016). Site reliability engineering: how Google runs production systems. " O'Reilly Media, Inc.".

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