Observability in Stateful Workloads: Strategies for Monitoring Persistent Services in Dynamic Cloud Environments
Keywords:
Cloud, Observability, Workloads, DynamicAbstract
Stateful workloads are central elements of any contemporary cloud-native design, but their permanence brings them special difficulties to observation. It introduces an elaborate system of tracking such workloads in terms of metric matching, failure propagation modeling, and cross-layer tracing continuity, outlined in this paper. In order to evaluate how useful the telemetry overhead, the anomaly correlation, and the dashboard-based diagnostics are, we conducted experiments spread over PostgreSQL, Kafka and Redis deployments. The conventional observability methods have proved within our means, to explain how the state changes and long-term associations. To minimize the time of diagnosis as well as enhance its reliability, we suggest optimized approaches to the reduction of metrics and unified dashboards. These plans enable DevOps teams to have scalable resilient operation tools.
Downloads
References
Niedermaier, S., Koetter, F., Freymann, A., & Wagner, S. (2019). On Observability and Monitoring of Distributed Systems – an industry interview study. In Lecture notes in computer science (pp. 36–52). https://doi.org/10.1007/978-3-030-33702-5_3
Thalheim, J., Rodrigues, A., Akkus, I. E., Bhatotia, P., Chen, R., Viswanath, B., Jiao, L., & Fetzer, C. (2017). Sieve: Actionable Insights from Monitored Metrics in Microservices. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1709.06686
Kratzke, N. (2022). Cloud-Native Observability: The Many-Faceted Benefits of Structured and Unified Logging—A Multi-Case Study. Future Internet, 14(10), 274. https://doi.org/10.3390/fi14100274
Li, B., Peng, X., Xiang, Q., Wang, H., Xie, T., Sun, J., & Liu, X. (2021). Enjoy your observability: an industrial survey of microservice tracing and analysis. Empirical Software Engineering, 27(1). https://doi.org/10.1007/s10664-021-10063-9
Cao, C., Blaise, A., Verwer, S., & Rebecchi, F. (2022). Learning state machines to monitor and detect anomalies on a kubernetes cluster. Proceedings of the 17th International Conference on Availability, Reliability and Security, 1–9. https://doi.org/10.1145/3538969.3543810
Shankar, S., & Parameswaran, A. G. (2022). Towards observability for production machine learning pipelines. Proceedings of the VLDB Endowment, 15(13), 4015–4022. https://doi.org/10.14778/3565838.3565853
Saha, A., Agarwal, P., Ghosh, S., Gantayat, N., & Sindhgatta, R. (2024). Towards Business Process Observability. Towards Business Process Observability, 257–265. https://doi.org/10.1145/3632410.3632435
Saminathan, M., Bhattacharyya, S., & Bairi, A. R. (2021, June 17). End-to-End observability in Cloud-Native systems: integrating distributed tracing and Real-Time analytics. Journal of Science & Technology. https://thesciencebrigade.com/jst/article/view/566
De Moraes Rossetto, A. G., Noetzold, D., Silva, L. A., & Leithardt, V. R. Q. (2024). Enhancing Monitoring Performance: A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models. Sensors, 24(13), 4212. https://doi.org/10.3390/s24134212
Lee, C., Yang, T., Chen, Z., Su, Y., & Lyu, M. R. (2023). EADRO: an End-to-End troubleshooting framework for microservices on multi-source data. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2302.05092
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
